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AI Accelerators

posted Aug 12, 2018, 2:49 AM by MUHAMMAD MUN`IM AHMAD ZABIDI   [ updated Aug 12, 2018, 3:40 AM ]


AI chip design projects:

 Company     Project
 Alibaba
 Amazon
 Baidu
 Google
 Microsoft
 Tencent

AI implementations and main weakness of each:
  • ASIC: slow to market, must find a large enough market to justify cost
  • CPU: very energy inefficient
  • GPU: great for training but very inefficient for inference
  • DSP: not enough performance, high cache miss rate


When to Use FPGAs

  • Transistor Efficiency & Extreme Parallelism
    • Bit-level operations
    • Variable-precision floating point
  • Power-Performance Advantage
    • >2x compared to Multicore (MIC) or GPGPU
    • Unused LUTs are powered off
  • Technology Scaling better than CPU/GPU
    • FPGAs are not frequency or power limited yet
    • 3D has great potential
  • Dynamic reconfiguration
    • Flexibility for application tuning at run-time vs. compile-time
  • Additional advantages when FPGAs are network connected ...
    • allows network as well as compute specialization

When to Use GPGPUs

  • Extreme FLOPS & Parallelism
    • Double-precision floating point leadership
    • Hundreds of GPGPU cores
  • Programming Ease & Software Group Interest
    • CUDA & extensive libraries
    • OpenCL
    • IBM Java (coming soon)
  • Bandwidth Advantage on Power
    • Start w/PCIe gen3 x16 and then move to NVLink
  • Leverage existing GPGPU eco-system and development base
    • Lots of existing use-Cases to build on
    • Heavy HPC investment in GPGPU When to Use GPGPUs

Bibliography
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