FPGAs, Deep Learning, Software Defined Networks and the Cloud: A Love Story Part 2

Re-Introduction to FPGAs

  1. Speed. Purpose built hardware provides better performance than hardware built for general purpose.
  2. Efficiency & Scale. Increased efficiency means serving more customers with less, allowing the hardware to service a larger scale of workloads.
  3. Cost. Improvements in speed, efficiency while reducing power consumption reduces cost.

Artificial Intelligence Workloads with FPGAs

  • Flexibility. FPGAs are ideal for adapting to rapidly evolving machine learning workloads as you can reprogram the chip for increased optimization depending on the workload you need to run on it.
  • Latency. FPGAs are well suited for latency-sensitive real-time inference requirements that are required in tasks like autonomous driving, speech recognition, anomaly detection and more.
  • Precision. FPGAs allow for increase precision for particular layers in your Deep Neural Networks (DNNs). As an example, NVIDIAs Pascal and Volta GPUs allow you to use both 8 and 16 bit integer values. For a DNN responsible for assessing a person’s sex you just need two values of male or female (3rd coming soon) making the 16 and 8 bit integer values overkill. With an FPGA, a DNN designer can model each layer in the net with 2 bits instead of 16 or 8 bits which has a significant impact on efficiency and performance of Tera-Operations per second as the chart in Figure 4 shows.
Figure 4: Narrow Precision Inference on FPGAs

Real-Time AI

Why Not Just an ASIC for AI?

“We can incorporate research innovations into the hardware platform quickly, (typically a few weeks), which is essential in this fast-moving space

The Wrap Up

  • Genomics. Using FPGA to reduced cost, power demands and storage requirements of genome processing, decrease genome analysis to minutes.
  • Aerospace & Defense . Using radiation-tolerant FPGAs along with intellectual property for image processing, waveform generation, and partial reconfiguration for Software Defined Radios.
  • Automotive. FPGAs are driving (pun intended) innovation of next-gen safety and autonomous driving systems and in-vehicle infotainment.
  • Consumer Electronics. Converged handsets (phones that can be PCs), digital flat panel displays, home networking, and residential set top boxes all powered by FPGAs.
  • Finance. FPGAs enabling dramatic improvements of risk modeling and analysis, transaction analysis for security and high frequency trading.
  • Video & Image Processing. FPGAs lowering non-recurring engineering costs, gamma correction, 2D/3D filtering, chroma re-sampling.
  • Online Search. 1,600 FPGA cluster running in production, dedicated to accelerating feature extraction of documents for the search engine.
Ross Freeman (1948–1989): Electrical Engineer and Inventor



Enterprise technologist with experience across cloud, artificial intelligence, machine learning, big-data and other cool technologies.

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