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Concepts Introduced in Chapter 7 Reasons for Adoption of Domain SpeciC Architectures Introduction Guidelines TPU Catapult Pixel Visual Core Issues Introduction Guidelines TPU Catapult Pixel Visual Core Issues Concepts Introduced in Chapter 7 Reasons for Adoption of Domain Specic Architectures End of Dennard scaling and slowing of Moore's Law means we need to lower the energy per operation to improve performance. guidelines for domain specic architectures (DSAs) If order-of-magnitude performance improvements are to be Google's Tensor Processing Unit obtained, we need to increase the number of arithmetic Microsoft's Catapult operations per instruction from one to hundreds. Google's Pixel Visual Core Conventional architecture innovations may not be a good crosscutting issues match to some domains. Many believe that future computers will consist of standard processors that can run conventional programs along with domain-specic processors that can very eciently perform only a narrow range of tasks. Introduction Guidelines TPU Catapult Pixel Visual Core Issues Introduction Guidelines TPU Catapult Pixel Visual Core Issues Factors That Can Make a DSA Cost Eective DSA Guidelines Find a domain whose demand for applications is for enough Use dedicated memories to minimize the distance over which data is moved. chips to justify the nonrecurring engineering (NRE) costs. Multi-level caches are expensive in area and energy for moving DSAs are best applied for small compute-intensive kernels of data. larger systems, where a signicant fraction of the computation Many domains have predictable memory access patterns with is done for some applications. little data reuse. This means that future computers may be much more DSA programmers understand their domain. Data movement can be more ecient with software controlled heterogeneous than current homogeneous multicore chips. memories. Architects must learn the application domain and algorithms. Invest resources saved from dropping advanced There must also be support for porting software to exploit microarchitectural optimizations into more arithmetic units these DSAs. and/or larger on-chip memories. Introduction Guidelines TPU Catapult Pixel Visual Core Issues Introduction Guidelines TPU Catapult Pixel Visual Core Issues DSA Guidelines (cont.) How Example DSAs Follow the Guidelines Use the easiest form of parallelism that matches the domain. Target domains for DSAs will have inherent parallelism. Need to exploit that parallelism and expose it to the software so it does not need to be automatically found by the hardware (no OoO execution). Reduce data size and type to the simplest needed for the domain. Many domains are memory bound. Can increase memory bandwidth and utilization by using narrower data types. Use a domain-specic programming language to port code to the DSA. Allow for easier exploitation of parallelism. Simplify porting of code to a DSA. Figure 7.3 The four DSAs in this chapter and how closely they followed the five guidelines. Pixel Visual Core typically has 2–16 cores. The first implementation of Pixel Visual Core does not support 8-bit arithmetic. Introduction Guidelines TPU Catapult Pixel Visual Core Issues Introduction Guidelines TPU Catapult Pixel Visual Core Issues Google's Tensor Processing Unit (TPU) TPU Architecture © 2019 Elsevier Inc. All rights reserved. 4 TPU is a coprocessor on the PCIe I/O bus from which it Google's rst ASIC DSA for its WSCs. receives instructions. Domain is for deep neural networks (DNNs). Has a large software managed on-chip memory, which consists Programmed using the TensorFlow language. of a 24 MiB Unied Buer. Goal is to improve cost-performance by a factor of 10 over Has o-chip 8 GiB DRAM for Weight Memory. GPUs. Matrix Multiply Unit (MMU) contains 256x256 (65,536) ALUs that can perform 8-bit multiply-and-adds on integers. Deployed in Google data centers since 2015. Reads and writes 256 values per clock cycle. Used for matrix multiplications and convolutions. Introduction Guidelines TPU Catapult Pixel Visual Core Issues Introduction Guidelines TPU Catapult Pixel Visual Core Issues TPU Block Diagram TPU ISA Has no PC as instructions are sent from the host CPU. TPU ISA contains CISC-like instructions, including a repeat eld to reduce interactions with the host CPU. example instructions Read_Host_Memory - reads data from CPU host memory into unied buer. Read_Weights - reads weights from weight memory into weight FIFO. MatrixMultiply/Convolve - causes MMU to operations on data in unied buer and produce its output to the accumulators. Activate - performs nonlinear function of the neuron from data in accumulators and stores results in unied buer. Write_Host_Memory - writes data from the unied buer into the CPU host memory. Figure 7.12 TPUIntroduction Block Diagram.Guidelines The PCIe bus isTPU Gen3 × 16. TheCatapult main computationPixel Visual part Core is the lightIssues-shaded MatrixIntroduction Guidelines TPU Catapult Pixel Visual Core Issues Multiply Unit in the upper-right corner. Its inputs are the medium-shaded Weight FIFO and the medium-shaded Unified Buffer and its outputTPU is the MMU medium-shaded Accumulators. The light-shaded Activation Unit performs the nonlinear TPU Die Floorplan functions on the Accumulators, which go to the Unified Buffer. The MMU uses a systolic array, which is a 2D collection of arithmetic units. Values ow from one© 2019 unit Elsevier to another. Inc. All rights reserved. 13 Data is only read once from memory and only written once to memory. Figure 7.14 Systolic data flow of the Matrix Multiply Unit. Figure 7.15 Floor plan of TPU die. The shading follows Figure 7.14. The light data buffers are 37%, the light computation units are 30%, the medium I/O is 10%, and the dark control is just 2% of the die. Control is much larger (and much more difficult to design) in a CPU or GPU. The unused white space is a consequence of the emphasis on © 2019 Elsevier Inc. All rights reserved. time to tape-out15 for the TPU. © 2019 Elsevier Inc. All rights reserved. 16 Introduction Guidelines TPU Catapult Pixel Visual Core Issues Introduction Guidelines TPU Catapult Pixel Visual Core Issues TPU Summary Microsoft's Catapult Use dedicated memories to minimize the distance over which data is moved. Has on-chip a 24 MiB unied buer that allows accessing 256 bytes each cycle, a weight FIFO, and a 4 MiB accumulators. Microsoft placed an FPGA on a PCIe bus board in data center Invest resources saved from dropping advanced servers. microarchitectural optimizations into more arithmetic units or Used FPGA exibility to tailor use for varying applications. bigger memories. Has 28 MiB of on-chip memory and 64K FPGAs have lower NRE costs than ASICs. 8-bit ALUs. FPGAs are slower than ASICs. Use the easiest form of parallelism that matches the domain. Exploits 2D SIMD parallelism with the 256x256 MMU. Key applications were to provide a CNN accelerator and to improve the performance of the Microsoft Bing search engine. Reduce data size and type to the simplest needed for the domain. Primarily does computation on 8-bit integers. Use a domain-specic programming language to port code to the DSA. Programs to control the TPU are written in the TensorFlow language. Introduction Guidelines TPU Catapult Pixel Visual Core Issues Introduction Guidelines TPU Catapult Pixel Visual Core Issues Catapult Architecture Catapult Software Programming is done in a hardware description language (e.g. Verilog). RTL code divided into shell (used across applications) and the role (application logic). Board has 32 MiB of ash memory and 8 GiB of DRAM. FPGA has 3926 18-bit ALUs, 5 MiB of on-chip memory, and 11 GB/s bandwidth to the DRAM. Designed to run at 25 W. Figure 7.20 Components of Catapult shell and role split of the RTL code. © 2019 Elsevier Inc. All rights reserved. 21 Introduction Guidelines TPU Catapult Pixel Visual Core Issues Introduction Guidelines TPU Catapult Pixel Visual Core Issues Catapult CNN Accelerator Catapult Feature Extraction Accelerator Figure 7.23 The architecture of FPGA implementation of the Feature Extraction stage. A hit vector, which describes the locations of query words in each document, is streamed into the hit vector preprocessing state machine Figure 7.21 CNNIntroduction Accelerator forGuidelines Catapult. The TPUInput Volume ofCatapult the left correspondPixel to Visual Layer[ Corei− 1] onand theIssues thenleft of split Figure into Introduction control and dataGuidelines tokens. TheseTPU tokens are issuedCatapult in parallelPixel to Visual the Core43 unique Issuesfeature state machines. 7.20, with NumFM[i− 1] corresponding to y and DimFM[i− 1] corresponding to z. Output Volume at theThe top featuremaps to- gathering network collects generated feature and value pairs and forwards them to the following Free-Form Layer[i], with z mapping to NumFM[i] and DimFM[i] mapping to x. The next figure shows the inside ofExpressions the Processing stage. Element (PE).Catapult Summary Google's Pixel Visual Core © 2019 Elsevier Inc. All rights reserved. 22 Use dedicated memories to minimize the distance over which © 2019 Elsevier Inc. All rights reserved. 24 data is moved. Has 5 MiB of on-chip memory. Invest resources saved from dropping advanced microarchitectural optimizations into more arithmetic units or Google multicore design has 2 to 16 cores. bigger memories. Has 3926 18-bit ALUs. Uses much smaller area and less energy than the TPU. Use the easiest form of parallelism that matches the domain. Domain area is vision processing. Exploits 2D SIMD parallelism for the CNN application and Example of a class of DSAs called image processing units MISD parallelism for search ranking. (IPUs) that analyze and modify an input image. Reduce data size and type to the simplest needed for the domain. Does computation on 8-bit integer to 64-bit FP values. Use a domain-specic programming language to port code to the DSA. Programming is done in Verilog. Introduction Guidelines TPU Catapult Pixel Visual Core Issues Introduction Guidelines TPU Catapult Pixel Visual Core Issues Pixel Visual Core Architecture Pixel Visual Core 2D Array of PEs Uses a 2D SIMD achitecture of independent processing elements (PEs), each containing 2 16-bit ALUs, 1 16-bit MAC unit, 10 16-bit registers, and 10 1-bit predicate registers.
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