Tiger SHARC Processor

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ABSTRACT The Tiger SHARC processor is the newest and most power member of this family which incorporates many mechanisms like SIMD, VLIW and short vector memory access in a single processor. This is the first time that all these have been combined in a real time processor. The TigerSHARC DSP is an ultra high performance static superscalar architecture that optimized for tele-communications infrastructure and other computationally demanding applications. The unique architecture combines elements of RISC, VLIW and standard DSP processors to provide native support for 8, 16,and 32-bit fixed, as well as floating point data types on single chip. Large on-chip memory, extremely high internal and external bandwidths and dual compute blocks provide the necessary capabilities to handle a vast array of computationally demanding, large signal processing tasks.

Contents 1. INTRODUCTION

1.1. Analog and digital signals 1.2. Signal processing 1.3. Digital signal processing 1.4. Development of DSP 1.5. Digital signal processors 2. ARCHITECTURE OF DIGITAL SIGNAL PROCESSORS 2.1. Von Neumann Architecture 2.2. Harvard Architecture 2.3. Super Harvard Architecture 3. THE TIGER SHARC PROCESSOR 3.1. Features 3.2. Benefits 3.3. Description 3.4. Tiger SHARC Processor families 3.5. Functional Block Diagram 3.5.1. Architectural Features 3.5.2. Adapts to evolving signal processing demands 3.5.3. Multiprocessor, general-purpose processing 3.5.4. Instruction Parallelism and SIMD operations 3.5.5. Independent, Parallel Computation Blocks 3.5.6. CLU (Communications Logic Unit) 3.5.7. Integer ALUs 3.5.8. Tiger SHARC memory Integration

3.5.9. Program Sequencer 3.5.10. Flexible Integrated Memory 3.5.11

DMA Controller

3.5.12. Link Ports 3.5.13. External Port

4. APPLICATIONS 5. ADVANTAGES 6. CONCLUSION 7. REFERANCES

1. INTRODUCTION 1.1 Analog and digital signals In many cases, the signal of interest is initially in the form of an analog electrical voltage or current, produced for example by a microphone or some other type of transducer. An analog signal must be converted into digital form before DSP techniques can be applied. An analog electrical voltage signal, for example, can be digitized using an electronic circuit called an analog-to-digital converter or ADC. This generates a digital output as a stream of binary numbers whose values represent the electrical voltage input to the device at each sampling instant.

1.2 Signal processing Signals commonly need to be processed in a variety of ways. For example, the output signal from a transducer may well be contaminated with unwanted electrical "noise". The electrodes attached to a patient's chest when an ECG is taken measure tiny electrical voltage changes due to the activity of the heart and other muscles. The signal is often strongly affected by "mains pickup" due to electrical interference from the mains supply. Processing the signal using a filter circuit can remove or at least reduce the unwanted part of the signal. Increasingly nowadays, the filtering of signals to improve signal quality or to extract important information is done by DSP techniques rather than by analog electronics.

1.3 Digital Signal Processing Digital signal processing (DSP) is the study of signals in a digital representation and the processing methods of these signals. DSP and analog signal processing are subfields of signal processing Digital Signal Processing is carried out

by mathematical operations. In comparison, word processing and similar programs merely rearrange stored data. This means that computers designed for business and other general applications are not optimized for algorithms such as digital filtering and Fourier analysis. Digital Signal Processors are microprocessors specifically designed

to handle Digital Signal Processing tasks. These devices have seen

tremendous growth in the last decade, finding use in everything from cellular telephones to advanced scientific instruments. In fact, hardware engineers use "DSP" to mean Digital Signal Processor, just as algorithm developers use "DSP" to mean Digital Signal Processing

1.4 Development of DSP The development of digital signal processing dates from the 1960's with the use of mainframe digital computers for number-crunching applications such as the Fast Fourier Transform (FFT), which allows the frequency spectrum of a signal to be computed rapidly. These techniques were not widely used at that time, because suitable computing equipment was generally available only in universities and other scientific research institutions.

1.5 Digital Signal Processors (DSPs) DSP processors are microprocessors designed to perform digital signal processing- the mathematical manipulation of digitally represented signals. The introduction of the microprocessor in the late 1970's and early 1980's made it possible for DSP techniques to be used in a much wider range of applications. However, general-purpose microprocessors such as the Intel x86 family are not ideally suited to the numerically-intensive requirements of DSP, and during the 1980's the increasing importance of DSP led several major electronics manufacturers

(such as Texas Instruments, Analog Devices and Motorola) to develop Digital Signal Processor chips - specialised microprocessors with architectures designed specifically for the types of operations required in digital signal processing. (Note that the acronym DSP can variously mean Digital Signal Processing, the term used for a wide range of techniques for processing signals digitally, or Digital Signal Processor, a specialised type of microprocessor chip). Like a general-purpose microprocessor, a DSP is a programmable device, with its own native instruction code. DSP chips are capable of carrying out millions of floating point operations per second, and like their better-known general-purpose cousins, faster and more powerful versions are continually being introduced. DSPs can also be embedded within complex "system-on-chip" devices, often containing both analog and digital circuitry. Advantage over other Microprocessors • Single cycle multiply-accumulate operations(MAC) • Real time performance • Flexibility and Reliability • Increased system performance • Reduced cost • Harvard architecture

2. Architecture of the Digital Signal Processor One of the biggest bottlenecks in executing DSP algorithms is transferring information to and from memory. This includes data, such as samples from the input signal and the filter coefficients, as well as program instructions, the binary codes that go into the program sequencer. For example, suppose we need to multiply two numbers that reside somewhere in memory. To do this, we must fetch three binary values from memory, the numbers to be multiplied, plus the program instruction describing what to do.

2.1 Von Neumann architecture Figure 1(a).shows how this seemingly simple task is done in a traditional microprocessor. This is often called a Von Neumann architecture, after the brilliant American mathematician John Von Neumann (1903-1957). Von Neumann guided the mathematics of many important discoveries of the early twentieth century. His many achievements include: developing the concept of a stored program computer, formalizing the mathematics of quantum mechanics, and work on the atomic bomb. As shown in (a), a Von Neumann architecture contains a single memory and a single bus for transferring data into and out of the central processing unit (CPU). Multiplying two numbers requires at least three clock cycles, one to transfer each of the three numbers over the bus from the memory to the CPU. We don't count the time to transfer the result back to memory, because we assume that it remains in the CPU for additional manipulation (such as the sum of products in an FIR filter). The Von Neumann design is quite satisfactory when you are content to execute all of the required tasks in serial. In fact, most computers today are of the Von Neumann

design. When an instruction is processed in such a processor, units of the processor not involved at each instruction phase wait idly until control is passed on to them. Increase in processor speed is achieved by making the individual units operate faster, but there is a limit on how fast they can be made to operate. So we need other architectures when very fast processing is required, and we are willing to pay the price of increased complexity.

2.2 Harvard architecture This leads us to the Harvard architecture, shown in (b). This is named for the work done at Harvard University in the 1940s under the leadership of Howard Aiken (1900-1973). As shown in this illustration, Aiken insisted on separate memories for data and program instructions, with separate buses for each. Since the buses operate independently, program instructions and data can be fetched at the same time, improving the speed over the single bus design. Most present day DSPs use this dual bus architecture.

2.3 Super Harvard Architecture(SHARC) Figure (c) illustrates the next level of sophistication, the Super Harvard Architecture. This term was coined by Analog Devices to describe the internal operation of their ADSP-2106x and new ADSP-211xx families of Digital Signal Processors. These are called SHARC® DSPs, a contraction of the longer term, Super Harvard ARChitecture. The idea is to build upon the Harvard architecture by adding features to improve the throughput. While the SHARC DSPs are optimized in dozens of ways, two areas are important enough to be included in Fig. (c): an instruction cache, and an I/O controller.

A handicap of the basic Harvard design is that the data memory bus is busier than the program memory bus. When two numbers are multiplied, two binary values (the numbers) must be passed over the data memory bus, while only one binary value (the program instruction) is passed over the program memory bus. To improve upon this situation, we start by relocating part of the "data" to program memory. For instance, we might place the filter coefficients in program memory, while keeping the input signal in data memory. (This relocated data is called "secondary data" in the illustration). At first glance, this doesn't seem to help the situation; now we must transfer one value over the data memory bus (the input signal sample), but two values over the program memory bus (the program instruction and the coefficient). In fact, if we were executing random instructions, this situation would be no better at all. However, DSP algorithms generally spend most of their execution time in loops. This means that the same set of program instructions will continually pass from program memory to the CPU. The Super Harvard architecture takes advantage of this situation by including an instruction cache in the CPU. This is a small memory that contains about 32 of the most recent program instructions. The first time through a loop, the program instructions must be passed over the program memory bus. This results in slower operation because of the conflict with the coefficients that must also be fetched along this path. However, on additional executions of the loop, the program instructions can be pulled from the instruction cache. This means that all of the memory to CPU information transfers can be accomplished in a single cycle: the sample from the input signal comes over the data memory bus, the coefficient comes over the program memory bus, and the program instruction comes from the instruction cache. In the jargon of the field, this efficient transfer of data is called a high memory-access bandwidth.

Figure 2. presents a more detailed view of the SHARC architecture, showing the I/O controller connected to data memory. This is how the signals enter and exit the system. For instance, the SHARC DSPs provides both serial and parallel communications ports. These are extremely high speed connections. For example, at a 40 MHz clock speed, there are two serial ports that operate at 40 Mbits/second each, while six parallel ports each provide a 40 Mbytes/second data transfer. When all six parallel ports are used together, the data transfer rate is an incredible 240 Mbytes/second. Just as important, dedicated hardware allows these data streams to be transferred directly into memory (Direct Memory Access, or DMA), without having to pass through the CPU's registers. The main buses (program memory bus and data memory bus) are also accessible from outside the chip, providing an additional interface to off-chip memory and peripherals. This allows the SHARC DSPs to use a four Gigaword (16 Gbyte) memory, accessible at 40 Mwords/second (160 Mbytes/second), for 32 bit data.

This type of high speed I/O is a key characteristic of DSPs. The overriding goal is to move the data in, perform the math, and move the data out before the next sample is available. Everything else is secondary. Some DSPs have on-board analog-to-digital and digital-to-analog converters, a feature called mixed signal. However, all DSPs can interface with external converters through serial or parallel ports.

At the top of the diagram are two blocks labeled Data Address Generator (DAG), one for each of the two memories. These control the addresses sent to the program and data memories, specifying where the information is to be read from or written to. In simpler microprocessors this task is handled as an inherent part of the program sequencer, and is quite transparent to the programmer. However, DSPs are designed to operate with circular buffers, and benefit from the extra hardware to manage them efficiently. This avoids needing to use precious CPU clock cycles to keep track of how the data are stored. For instance, in the SHARC DSPs, each of the two DAGs can control eight circular buffers. This means that each DAG holds 32 variables (4 per buffer), plus the required logic. Some DSP algorithms are best carried out in stages. For instance, IIR filters are more stable if implemented as a cascade of biquads (a stage containing two poles and up to two zeros). Multiple stages require multiple circular buffers for the fastest operation. The DAGs in the SHARC DSPs are also designed to efficiently carry out the Fast Fourier transform. In this mode, the DAGs are configured to generate bitreversed addresses into the circular buffers, a necessary part of the FFT algorithm. In addition, an abundance of circular buffers greatly simplifies DSP code generationboth for the human programmer as well as high-level language compilers, such as C. The data register section of the CPU is used in the same way as in traditional microprocessors. In the ADSP-2106x SHARC DSPs, there are 16 general purpose registers of 40 bits each. These can hold intermediate calculations, prepare data for the math processor, serve as a buffer for data transfer, hold flags for program control, and so on. If needed, these registers can also be used to control loops and counters; however, the SHARC DSPs have extra hardware registers to carry out many of these functions.

The math processing is broken into three sections, a multiplier, an arithmetic logic unit (ALU), and a barrel shifter. The multiplier takes the values from two registers, multiplies them, and places the result into another register. The ALU performs addition, subtraction, absolute value, logical operations (AND, OR, XOR, NOT), conversion between fixed and floating point formats, and similar functions. Elementary binary operations are carried out by the barrel shifter, such as shifting, rotating, extracting and depositing segments, and so on. A powerful feature of the SHARC family is that the multiplier and the ALU can be accessed in parallel. In a single clock cycle, data from registers 0-7 can be passed to the multiplier, data from registers 8-15 can be passed to the ALU, and the two results returned to any of the 16 registers. There are also many important features of the SHARC family architecture that aren't shown in this simplified illustration. For instance, an 80 bit accumulator is built into the multiplier to reduce the round-off error associated with multiple fixedpoint math operations. Another interesting feature is the use of shadow registers for all the CPU's key registers. These are duplicate registers that can be switched with their counterparts in a single clock cycle. They are used for fast context switching, the ability to handle interrupts quickly. When an interrupt occurs in traditional microprocessors, all the internal data must be saved before the interrupt can be handled. This usually involves pushing all of the occupied registers onto the stack, one at a time. In comparison, an interrupt in the SHARC family is handled by moving the internal data into the shadow registers in a single clock cycle. When the interrupt routine is completed, the registers are just as quickly restored. This feature allows step 4 on our list (managing the sample-ready interrupt) to be handled very quickly and efficiently.

SHARC has 32/42 bit floating and fixed point core.DMA controller and duel ported SRAM to move data into and out of memory without wasting core cycles. It has high performance computation unit. It has four bus performances. They include fetch next instruction, access 2 data values, performs DMA for I/O device.

3. The TigerSHARC Processor Tiger sharc processors provide the highest performance density for multiplexing applications with peak performance and well above a billion floating point operations per second. One Gbyte/sec multiprocessing link ports gluelessly multiple Tiger sharc processors, and versions are available with up to 24 Mbits of integrated, on chip memory.

Keeping pace with the accelerating march of architectural innovation in DSPs, Analog devices (ADI) unveiled its third generation floating point DSP,TIGERSHARC. There architect Jose Fridman described a complex, high performance VLIW-based design incorporating unusually extensive singleinstruction, multiple data (SIMD) capabilities. Unlike its predecessors, which are primarily aimed at application demanding floating point arithmetic, TigerSHARc has excellent fixed point capabilities and is better described as 16-bit fixed point DSP with floating point support than as a floating point DSP. The TigerSHARC® Processor provides leading-edge system performance while keeping the highest possible flexibility in software and hardware development. The TigerSHARC Processor's balanced architecture utilizes characteristics of RISC, VLIW, and DSP to provide a flexible, "all software" approach that adds capacity while reducing costs and bills of material.

3.1 FEATURES •

Static Superscalar Architecture

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Two 32 bit MACs per cycle with 80-bit accumulation

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Eight 16-bit MACs per cycle with 40-bit accumulation

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Two 16-bit complex MACs per cycle

•

Add-subtract instruction and bit reversal in hardware for FFTs

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64-bit generalised bit manipulation unit

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Two billion MACs per second at 250 MHz

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2 billion 16-bit MACs

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500 million 32-bit MACs

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12 GB/s of internal memory bandwidth for data and code

•

500 MHz, 2.0 ns instruction cycle rate.

•

12 Mbits of internal on-chip –DRAM memory

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Dual computation blocks, each containing an ALU,a multiplier, a shifter and a register file

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Dual integer ALUs, providing and data addressing and pointer manipulation

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Single precision IEEE 32-bit and extended bit precision 40-bit floating point data formats and 8-,16-,32- and 64 bit fixed point data formats.

•

Integrated

I/O

include

14

channel

DMA

controller,

external

port,progamable flag pins, two timers and timer expired pin for system integration.

3.2 Key Benefits

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Provides high performance static Superscalar DSP operations, optimized for large, demanding multiprocessor DSP applications

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Performs exceptionally well on DSP algorithm and I/O benchmarks

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Supports low overhead DMA transfers between Internal memory, external memory, memory mapped peripherals, host processors and other DSPs

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Eases programming through extremely flexible instruction set and high level language friendly DSP architecture

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Enables scalable multiprocessing systems with low communication overhead.

3.3 DESCRIPTION

TigerSHARC processor is an ultrahigh performance, static superscalar processor optimized for large signal processing tasks and communication infrastructure. The DSP combines very memory widths with dual computation blocks-supporting floating point (IEEE 32-bit and extended precision 40-bit) and fixed point (8-,16-,32-,64- bits) processing to set a new standard of performance for digital signal processors. The TigerSHARC

static superscalar architecture lets the DSP execute up to four instructions each cycle, performing 24 fixed point (16-bit) operations. Four independent 128-bit wide internal data buses, each connecting to the six 2M bit memory banks, enable quad –word data, instruction, and I/O address and provide 28 Gbytes per second of internal memory bandwidth.

Like its competititor Texas Instruments’ TMC320C64x, TigerSHARC uses a very long instruction word (VLIW) load/store architecture.TigerSHARC executes as any as four instructions per cycle with its interlocking ten-stage pipeline

and dual computation blocks. Each block contains a multiplier, an ALU, and a 64 –bit shifter and can perform one 32-*32 bit or four 16-*16-bit multiply – accumulates (MAC) per cycle.

TigerSHARC is aimed at telecommunications infrastructure applications, such as cellular telephone base stations. As illustrated in fig. the TigerSHARC architecture contains a program control unit two computation units,

two address generators memory various peripherals and a DMA controller. With its VLIW architecture TigerSHARC is capable of executing up to four instructions in a single cycle, and its SIMD features enable it to perform arithmetic operations on multiple 32-bit floating point values or multiple 32-,16- or 8-bit fixed point values.

Each of TigerSHARC’s computation units can perform two 32*32=64-bit fixed point multiply-accumulates in a single cycle, using two operands each made up of two concatenated 32-bit registers. Thus using both computation units TigerSHARc can perform four 32*32=64-bit fixed –point multiply-accumulate operations in a single cycle. Alternatively, TigerSHARC can perform two 32-bit floating point MAC operations per cycle. In fixed point DSP applications, the most common word width is 16bits.With four 16-bit fixed point elements concatenated in two 32-bit registers, one computation unit can in a single cycle perform four 16*16=32-bit multiplyaccumulate operations (with 8 guard bits each to avoid overflow)-twice as many as any currently available fixed or floating point DSP can perform.

TigerSHARC uses SIMD features at two levels-two separate computation units that each operate on SIMD operands .Fig illustrates how the two SIMD computation units divide the registers into different data sizes. TigerSHARC is the first of the new wave of VLIW –based DSPs to provide extensive SIMD capabilities. This approach provides greater parallelism than that of its Texas Instruments competitors.

On-chip memory is divided into three banks: one for soft-ware and two for data. ADI will not disclose the amount of on-chip memory in the first

TigerSHARC devices, but we expect that the vendor will continue to be generous with on-chip memory; the predecessor SHARC and Hammerhead devices include 68K to 512K of on-chip memory. When moving 64-bit or 128-bit data, TigerSHARC transfers data from consecutive memory locations to consecutive data registers, or vice versa. The smallest amount of data that can be transferred is 32 bits. If TigerSHARC programs use word sizes of 8 or 16 bits in a DSP algorithm, they cannot access individual words; any load or store will transfer at least four 8-bit or two 16-bit words. The chip includes a data alignment buffer and a short data alignment buffer that allow 64 or 128 bits of data to be transferred from (but not to) any memory location aligned on a 16-bit word boundary. TigerSHARC provides more flexibility than most processors with SIMD features, which often require that data be aligned at memory locations divisible by the size of the data transfer.

Data is transferred between the computation units and on-chip memory in blocks of 32-,64-,or128-bits.When moving 64-bit or 128-bit data,TigerSHARC transfers data from consecutive memory locations to consecutive data registers, or vice versa. The smallest amount of data that can be transferred is 32-bits.If TigerSHARC programs use word size of 8 or 16 bits in a DSP algorithm ,they cannot access individual words, any load or store will transfer at least four 8-bit or two 16-bit words. The chip includes a data alignment buffer and a short data alignment buffer that allow 64 or 128 bits of data to be transferred from (but not to be) any memory location aligned on a 16-bit word boundary.TigerSHARC provides more flexibility than most processors with SIMD features, which often require that data be aligned at memory locations divisible by the size of the data transfer.

3.4

TigerSHARC Processor families

Nr

Processor Name

Description

Manufacturer

1

ADSP-TS101-S

ADSP-TS101S TigerSHARC DSP

Analog Devices

2

ADSP-TS101S

300 MHz TigerSHARC Processor with 6 Mbit on-chip SRAM

Analog Devices

ADSP-TS101SAB1000 ADSP-TS101SAB1100

300 MHz TigerSHARC Processor with 6 Mbit on-chip SRAM

Analog Devices

300 MHz TigerSHARC Processor with 6 Mbit on-chip SRAM

Analog Devices

5

ADSP-TS201S

500/600 MHz TigerSHARC Processor with 24 Mbit on-chip Analog Devices embedded DRAM

6

ADSP-TS201SABP- 500/600 MHz TigerSHARC Processor with 24 Mbit on-chip Analog Devices embedded DRAM 050

7

ADSP-TS202S

.

3 4

ADSP-TS202SABP050 ADSP-TS202SABP9 X ADSP-TS202SABP10 X 8

11

ADSP-TS203S

ADSP-TS203SABP050 ADSP-TS203SABP13 X ADSP-TS203SABP14 X 12

500 MHz TigerSHARC Processor with 12 Mbit on-chip embedded DRAM

Analog Devices

500 MHz TigerSHARC Processor with 12 Mbit on-chip embedded DRAM

Analog Devices

TigerSHARC Embedded Processor

Analog Devices

TigerSHARC Embedded Processor

Analog Devices

500 MHz TigerSHARC Processor with 4 Mbit on-chip embedded DRAM

Analog Devices

500 MHz TigerSHARC Processor with 4 Mbit on-chip embedded DRAM

Analog Devices

TigerSHARC Embedded Processor

Analog Devices

TigerSHARC Embedded Processor

Analog Devices

3.5. FUNCTIONAL BLOCK DIAGRAM

3.5.1 Architectural Features

Flexibility without compromise—the TigerSHARC® Processor provides leading-edge system performance while keeping the highest possible flexibility in software and hardware development. The TigerSHARC Processor's balanced architecture utilizes characteristics of RISC, VLIW, and DSP to provide a flexible, "all software" approach that adds capacity while reducing costs and bills of material. The TigerSHARC® Processor is an ultra-high performance static superscalar

DSP

optimized

for

multi-processing

applications

requiring

computationally demanding large signal processing tasks. This document describes the key features of the TigerSHARC Processor architecture that combine to offer the highest performance, flexibility, efficiency and scalability available to equipment manufacturers in the marketplace today 3.5.2 Adapts to evolving signal processing demands The TigerSHARC's unique ability to process 1-, 8-, 16- and 32-bit fixed-point as well as floating-point data types on a single chip allows original equipment manufacturers to adapt to evolving telecommunications standards without encountering the limitations of traditional hardware approaches .Having the highest performance

DSP

for

communications

infrastructure

and

multiprocessing

applications available, TigerSHARC allows wireless infrastructure manufacturers to continue evolving their design to meet the needs of their target system, while deploying a highly optimized and effective Node B solution that will realize significant overall cost savings.

3.5.3 Multiprocessor, general-purpose processing

The TigerSHARC Processor's balanced architecture optimizes system, cost, power, and density. A single TigerSHARC Processor, with its large on-chip memory, zero overhead DMA engine, large I/O throughput, and integrated multiprocessing support, has the necessary integration to be a complete node of a multiprocessing system. This enables a multiprocessor network exclusively made up of TigerSHARCs without any expensive and power consuming external memories or logic. 3.5.4 Instruction Parallelism and SIMD Operation As a static superscalar DSP, the TigerSHARC Processor core can execute simultaneously from one to four 32-bit instructions encoded in a single instruction line. With a few exceptions, an instruction line, whether it contains one, two, three or four 32-bit instructions, executes with a throughput of one cycle in an eight-deep processor pipeline. The TigerSHARC Processor has a set of instruction parallelism rules that programmers must follow when encoding an instruction line. In general, the selection of instruction the DSP can execute in parallel each cycle depends on the instruction line resources each requires and on the source and destination of registers used. The programmer has direct control of the three core components - the IALU, the Computation Blocks, and the Program Sequencer. In most cases the TigerSHARC Processor has a two-cycle execution pipeline that is fully interlocked, so whenever a computation result is unavailable for another operation dependent on it, stall cycles are automatically inserted. Efficient

programming with dependency-free instructions can eliminate most computational and memory transfer dependencies. All of the instruction parallel rules and data dependencies are documented in the TigerSHARC Processor User's Guide. The TigerSHARC Processor also has the capability of supporting singleinstruction, multiple-data SIMD operations through the use of both Computational Blocks in parallel as well as the use of SIMD specific computations. The programmer has the option of directing both Computation Blocks to operate on the same data (broadcast distribution) or different data (merged distribution). In addition, each Computation Block can execute four 16-bit or eight 8-bit SIMD computations in parallel. 3.5.5. Independent, Parallel Computation Blocks As mentioned above, the TigerSHARC Processor has two Computation Blocks that can operate either independently, in parallel or as a SIMD engine. The DSP can issue up to two compute instructions per Computation Block per cycle, instructing the ALU, multiplier or shifter to perform independent, simultaneous operations. The Computation Blocks each contain four computational units, an ALU, a multiplier, a 64-bit shifter, a CLU (ADSP-TS201S only) and a 32-bit register file. The 32-bit word, multi-ported register files are used for transferring data between the computational units and data buses, and for storing intermediate results. Instructions can access the registers in the register file individually (word-aligned) or in sets of two (dual-aligned) or four (quad-aligned). The ALU performs a standard set of arithmetic operations in both fixed-point and floating-point formats, while also performing logic operations. The multiplier performs both fixed-point and floating-

point multiplication as well as fixed-point multiply and accumulates. The 64-bit shifter performs logical and arithmetic shifts, bit and bit-stream manipulation, and field deposit and extraction. 3.5.6. CLU (Communications Logic Unit) The CLU on the ADSP-TS201S is a 128-bit unit which houses enhanced acceleration instructions specifically targeted at increasing the amount of Complex Multiplies per cycle and improving the Decoding efficiency of the TigerSHARC device. The CLU is not available on the ADSP-TS202S and ADSP-TS203S. 3.5.7 Integer ALUs The TigerSHARC Processor has two integer ALUs (IALUs) that provide powerful address generation capabilities and perform many general-purpose integer operations. Each IALU has a multi-ported 31-word register file. As address generators, the IALUs perform immediate or indirect (pre- and post-modify) addressing. They perform modulus and bit-reverse operations with no constraints placed on memory addresses for data buffer placement. Each IALU can specify either a single, dual- or quad- word access from memory. The TigerSHARC Processor IALUs enable implementation of circular buffers in hardware. Circular buffers facilitate efficient programming of delay lines and other data structures required in digital signal processing, and they are commonly used in digital filters and Fourier transforms. Each IALU provides registers for four circular buffers, so applications can set up a total of eight circular buffers. The IALUs handle address pointer wraparound automatically, reducing overhead, increasing performance, and simplifying implementation.

Circular buffers can start and end at any memory location. Because the IALU's computational pipeline is one cycle deep, in most cases integer results are available in the next cycle. Hardware (register dependency check) causes a stall if a result is unavailable in a given cycle. 3.5.8 TigerSHARC Memory Integration The large on-chip memory is divided into three separate blocks of equal size. Each block is 128-bits wide, offering the quad word structure and four addresses for every row. For data accesses, the processor can address one 32-bit word or two 32-bit words (long) or four 32-bit words (quad) and transfer it to/from a single computational unit or to both in a single processor cycle. The user only has to care that the start addresses are either modulo two or modulo four addresses when fetching long words and quad words. In applications that require computing data of a delay line in which the start address of the variable does not match the modulo requirements, or in other applications that require unaligned data fetches a data alignment buffer (DAB) is provided. Once the DAB is filled, quad word fetches can be made from it.Besides the internal memory, the TigerSHARC can access up to four giga words of memory. The memory map is given in Figure

3.5.9 Program Sequencer The TigerSHARC Processor Program Sequencer manages program structure and program flow by supplying addresses to memory for instruction fetches. Contained within the Program Sequencer, the Instruction Alignment Buffer (IAB) caches up to five fetched instruction lines waiting to execute. The Program Sequencer extracts an instruction line from the IAB and distributes it to the appropriate core component for execution. Other Program Sequencer functions include; determining flow according to instructions such as JUMP, CALL, RTI and RTS, decrement the loop counters, handle hardware interrupts and using branch prediction and 128-entry Branch Target Buffer (BTB) to reduce branch delays for efficient execution of conditional and unconditional branch instructions. 3.5.10. Flexible Integrated Memory The ADSP-TS20xS family has three memory variants. The ADSPTS201S has 24Mbits of on-chip embedded DRAM memory, divided into six blocks of 4Mbits (128 K words X 32-bits); the ADSP-TS202S has 12Mbits of on-chip embedded DRAM memory, divided into six blocks of 2Mbits (64 K words X 32bits); the ADSP-TS203S has 4Mbits of on-chip embedded DRAM memory, divided into four blocks of 1Mbit (16 K words X 32-bits). On all variants, each block can store program memory, data memory or both, so programmers can configure the memory to suit their specific needs. The six memory blocks connect to the four 128bit wide internal buses through a crossbar connection, enabling four memory transfers in the same cycle. The internal bus architecture of the ADSP-TS20xS family provides a total memory bandwidth of 32 Gbytes/second, enabling the core and I/O to access twelve 32-bit data words four 32-bit instructions per cycle.

3.5.11. DMA Controller The TigerSHARC Processor on-chip DMA controller, with fourteen DMA channels, provides zero-overhead data transfers without processor intervention. The DMA controller operates independently and invisibly to the DSP's core, enabling DMA operations to occur while the core continues to execute program instructions. The DMA controller performs routine functions such as external port block transfers, link port transfers and AutoDMA transfers as well as additional features such as Flyby transfers, DMA chaining and Two-dimensional transfers. 3.5.12. Link Ports The ADSP-TS201S and ADSP-TS202S have four full-duplex link ports each providing four-bit receive and four-bit transmit I/O capability, using Low-Voltage, Differential-Signal (LVDS) technology. With the ability to operate at a double data rate running at 500 MHz, each link can support up to 500 Mbytes per second per direction, for a combined maximum throughput of 4 Gbytes per second. The ADSP-TS203S has two full-duplex link ports each providing four-bit receive and four-bit transmit I/O capability, using Low-Voltage, Differential-Signal (LVDS) technology. With the ability to operate at a double data rate running at 250 MHz, each link can support up to 500 Mbytes per second per direction, for a combined maximum throughput of 4 Gbytes per second. Each Link Port has its own triple-buffered quad-word input and doublebuffered quad-word output registers. The DSP's core can write directly to a Link Port's transmit register and read from a receive register, or the DMA controller can perform DMA transfers through eight dedicated Link Port DMA channels.

3.5.13. External Port

The external port on TigerSHARC Processor is 64 bits wide and runs up to 125MHz. Using the external port, up to 8 TigerSHARC Processor's, a host and global memory can be shared without any external logic. This is the second way, in addition to link ports, that TigerSHARC DSP offers support for multiprocessor systems. SDRAM and SBSRAM controllers allow for a glueless interface to these types of memories. The external port also supports a fly by mode which allows a host to access a global shared memory.

4. Applications At a 250 MHz clock rate, the ADSP-TS101S [TigerSHARC] offers a DSP industry-best 1500 MFLOPS peak performance and has native support for 8, 16, 32, and 40-bit data types. With a 1.5 watt typical power dissipation, 6 Mbits of on-chip memory, 14 channel zero-overhead DMA engine, integrated SDRAM controller, parallel host interface, cluster multiprocessing support, and link port multiprocessing support, the TigerSHARC is ideal for heat sensitive multiprocessing applications. Here are some of the target applications for floating-point DSPs: "TigerSHARC's exceptional speed and functionality are suited for applications in: Defense - sonar, radar, digital maps, munitions guidance Medical - ultrasound, CT scanners, MRI, digital X-ray Industrial systems - data acquisition, control, test, and inspection systems Video processing - editing, printers, copiers Wireless Infrastructure - GSM, EDGE, and 3G cellular base stations."

5. Advantages of Tiger SHARC Processor

The Analog Devices TigerSHARC® Processor architecture provides the greatest marriage of performance and flexibility enabling the most cost effective solution for baseband processing and other applications within the Wireless Infrastructure market space today. Wireless Infrastructure manufacturers can consider many approaches when developing baseband modem solutions for third generation wireless communications networks (3G), however the TigerSHARC Processor architecture provides the balance of attributes required to satisfy the entire range of challenges facing their 3G deployments. The TigerSHARC Processor is the heart of a software defined solution for baseband modems where all of the implementation occurs in software rather than in hardware as is the approach taken by ASIC and other competing DSP solutions. The TigerSHARC Processor allows for the infrastructure vendor to establish a single baseband processing platform for all of the 3G standards with easily implemented software changes to update functionality and speed time to market. The very powerful architecture of the TigerSHARC, combining the best elements of RISC and DSP cores, is highly suited to deliver the performance required for upcoming applications in 3G mobile communications, xDSL technologies and imaging systems. The Static Superscalar architecture maintains determinism for security-sensitive applications and the high number of internal registers allows the efficient use of a high-level language, speeding up the development process of the designers.

6. Conclusion As a result of its "Load Balancing" capabilities, high internal and external bandwidth, large integrated memory and unmatched level of flexibility, the

TigerSHARC Processor proves to be an unconventional but extremely effective solution for baseband signal processing. In future generations of the TigerSHARC Processor we intend to continue the trend towards reduced systems cost and component count while increasing the functionality of the solution through clock speed enhancements and an expanded instruction set.

7. References • www.analog.com/processors/tigersharc •

www.analog.com/processors/teaching Resources

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www.ener.ucalgory.co/People/Smith/ECE-ADI-PROJECT

• www.answers.com