BEANNA: A Binary-Enabled Architecture for Neural Network Acceleration

TL;DR

BEANNA is a specialized neural network accelerator that efficiently processes both floating point and binary layers, significantly improving throughput and reducing memory and energy use for mobile and embedded AI applications.

Contribution

This paper introduces BEANNA, a novel hardware architecture with a 16x16 systolic array that seamlessly switches between floating point and binary neural network processing.

Findings

Achieves 52.8 GigaOps/sec in high precision mode

Reaches 820 GigaOps/sec in binary mode

Provides 194% throughput increase with minimal accuracy loss

Abstract

Modern hardware design trends have shifted towards specialized hardware acceleration for computationally intensive tasks like machine learning and computer vision. While these complex workloads can be accelerated by commercial GPUs, domain-specific hardware is far more optimal when needing to meet the stringent memory, throughput, and power constraints of mobile and embedded devices. This paper proposes and evaluates a Binary-Enabled Architecture for Neural Network Acceleration (BEANNA), a neural network hardware accelerator capable of processing both floating point and binary network layers. Through the use of a novel 16x16 systolic array based matrix multiplier with processing elements that compute both floating point and binary multiply-adds, BEANNA seamlessly switches between high precision floating point and binary neural network layers. Running at a clock speed of 100MHz, BEANNA achieves a peak throughput of 52.8 GigaOps/second when operating in high precision mode, and 820 GigaOps/second when operating in binary mode. Evaluation of BEANNA was performed by comparing a hybrid network with floating point outer layers and binary hidden layers to a network with only floating point layers. The hybrid network accelerated using BEANNA achieved a 194% throughput increase, a 68% memory usage decrease, and a 66% energy consumption decrease per inference, all this at the cost of a mere 0.23% classification accuracy decrease on the MNIST dataset.