Flexibility
| Autor: | Gordon Raymond Chiu, Andrew Ling, Davor Capalija, Andrew Bitar, Mohamed S. Abdelfattah |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
010302 applied physics
Flexibility (engineering) Artificial neural network Computer science business.industry Deep learning Inference CAD 02 engineering and technology 01 natural sciences 020202 computer hardware & architecture High-level design Computer architecture 0103 physical sciences Vectorization (mathematics) 0202 electrical engineering electronic engineering information engineering Artificial intelligence Physical design business |
| Zdroj: | ISPD |
| DOI: | 10.1145/3177540.3177561 |
| Popis: | Deep learning inference has become the key workload to accelerate in our AI-powered world. FPGAs are an ideal platform for the acceleration of deep learning inference by combining low-latency performance, power-efficiency, and flexibility. This paper examines the flexibility aspect, and its impact on FPGA design methodology, physical design tools and CAD. We describe the degrees of flexibility required for creating efficient deep learning accelerators. We quantify the varying effects of precision, vectorization, and buffering on both performance and accuracy, and show how the FPGA can yield superior performance through architecture customization tuned for a specific neural network. We describe the need for abstraction and propose solutions in modern FPGA design flows to enable the rapid creation of these customized accelerator architectures for deep learning inference acceleration. Finally, we examine the implications on physical design tools and CAD. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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