FPGA Neural Network Accelerator

✓ Completed

Technologies

VerilogFPGAPythonTensorFlowComputer ArchitectureDigital Design
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FPGA Neural Network Accelerator

FPGA Neural Network Accelerator

A custom hardware accelerator designed for efficient neural network inference on edge devices. This project explores the intersection of computer architecture and machine learning, focusing on optimizing both performance and power consumption.

Architecture Overview

The accelerator implements a systolic array architecture optimized for matrix multiplication operations that are fundamental to neural network computations.

Design Features

  • Parallel Processing Units: 16x16 systolic array for matrix operations
  • Custom Memory Hierarchy: Optimized for neural network data access patterns
  • Quantization Support: 8-bit and 16-bit fixed-point arithmetic
  • Dynamic Reconfiguration: Adaptable to different network architectures

Performance Optimizations

Memory Access Optimization

  • Data Reuse: Maximizes utilization of on-chip memory
  • Prefetching: Anticipates memory access patterns to reduce latency
  • Compression: On-the-fly weight compression to reduce bandwidth requirements

Computational Efficiency

  • Pipeline Design: Deep pipeline for maximum throughput
  • Parallel Execution: Multiple operations per clock cycle
  • Energy Optimization: Clock gating and power islands for unused components

Benchmarks and Results

Tested on standard neural network benchmarks:

CNN Performance (ResNet-18)

  • Throughput: 1,200 images/second at 100 MHz
  • Power Consumption: 2.3W (10x more efficient than GPU)
  • Accuracy: 99.2% of full-precision baseline

Edge Deployment

  • Latency: 0.83ms per inference
  • Energy: 1.9mJ per inference
  • Memory: 512KB on-chip storage

Applications

The accelerator has been successfully deployed in:

  • Autonomous Vehicles: Real-time object detection
  • IoT Devices: Edge AI processing with battery constraints
  • Robotics: Low-latency perception systems

Technical Innovation

Novel Contributions

  1. Adaptive Quantization: Dynamic bit-width adjustment based on layer sensitivity
  2. Hierarchical Memory Design: Three-level memory hierarchy optimized for NN workloads
  3. Runtime Reconfiguration: Hardware adaptation to different network topologies

Synthesis Results

  • Target FPGA: Xilinx Zynq UltraScale+
  • Logic Utilization: 78% LUTs, 65% DSP blocks
  • Maximum Frequency: 150 MHz
  • On-chip Memory: 512KB BRAM

Future Enhancements

Currently investigating:

  • Transformer Architecture Support: Optimizations for attention mechanisms
  • Sparse Network Acceleration: Hardware support for pruned networks
  • Multi-precision Arithmetic: Dynamic precision scaling during inference

Publications

This work contributed to a paper submitted to the International Symposium on Computer Architecture (ISCA) focusing on energy-efficient neural network acceleration in edge computing environments.