# Field-Programmable Gate Array Architecture for Deep Learning: Survey & Future Directions

**Authors:** Andrew Boutros, Aman Arora, Vaughn Betz  
**arXiv:** [2404.10076](https://arxiv.org/abs/2404.10076)  
**Date:** 2024-04-15  
**Summary page:** https://www.summarizepaper.com/en/paper/2404.10076v1/

## Key points

- Field-Programmable Gate Arrays (FPGAs) are crucial for accelerating deep learning (DL) inference tasks
- FPGAs offer reprogrammability and direct hardware execution, leading to lower latency and higher energy efficiency compared to CPUs and GPUs
- FPGAs allow customization of processing pipelines and memory hierarchies, reducing development time and costs associated with custom chips
- FPGA architectures are continuously evolving to deliver enhanced performance for DL tasks, including model-specific dataflow approaches and specialized blocks for tensor computations
- Hybrid devices combining processors with accelerator blocks show promising potential in enhancing system performance for DL tasks

## Summary

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In the realm of deep learning (DL), Field-Programmable Gate Arrays (FPGAs) are emerging as a crucial tool for accelerating DL inference tasks. As DL continues to play a pivotal role in various applications within datacenters and at the edge, the need for specialized hardware to meet the performance demands of cutting-edge DL models is evident. FPGAs offer a unique combination of reprogrammability and direct hardware execution that makes them well-suited for enhancing DL inference capabilities. By allowing customization of processing pipelines and memory hierarchies, FPGAs can achieve lower latency and higher energy efficiency compared to general-purpose CPUs and GPUs, all while significantly reducing development time and costs associated with custom chips. Moreover, their versatile high-speed IOs enable seamless interfacing with networks and external sensors, making them ideal for both datacenter and edge computing scenarios. Recognizing the increasing importance of DL workloads, FPGA architectures are continuously evolving to deliver enhanced performance for DL tasks. In this comprehensive survey conducted by Andrew Boutros, Aman Arora, and Vaughn Betz, both academic and industrial advancements in FPGA architecture tailored for DL are explored. The article begins with an overview of FPGA architecture fundamentals, highlighting how different components contribute to strengths and weaknesses in DL applications. The discussion then delves into various styles of DL inference accelerators implemented on FPGAs, ranging from model-specific dataflow approaches to software-programmable overlay designs. The survey also covers specific enhancements geared towards optimizing traditional FPGA building blocks such as logic blocks, arithmetic circuitry, on-chip memories, as well as new in-fabric specialized blocks designed specifically for accelerating tensor computations. Furthermore, hybrid devices that combine processors with coarse-grained accelerator blocks featuring FPGA-like interconnects and networks-on-chip are examined. These hybrid architectures showcase promising potential in enhancing overall system performance for DL tasks. Overall, this detailed exploration sheds light on the current landscape of FPGA architecture enhancements tailored for deep learning applications while also pointing towards exciting future research directions in this rapidly evolving field.