FPGA-Based BNN Architecture in Time Domain with Low Storage and Power Consumption

Abstract

With the increasing demand for convolutional neural networks (CNNs) in many edge computing scenarios and resource-limited settings, researchers have made efforts to apply lightweight neural networks on hardware platforms. While binarized neural networks (BNNs) perform excellently in such tasks, many implementations still face challenges such as an imbalance between accuracy and computational complexity, as well as the requirement for low power and storage consumption. This paper first proposes a novel binary convolution structure based on the time domain to reduce resource and power consumption for the convolution process. Furthermore, through the joint design of binary convolution, batch normalization, and activation function in the time domain, we propose a full-BNN model and hardware architecture (Model I), which keeps the values of all intermediate results as binary (1 bit) to reduce storage requirements by 75%. At the same time, we propose a mixed-precision BNN structure (model II) based on the sensitivity of different layers of the network to the calculation accuracy; that is, the layer sensitive to the classification result uses fixed-point data, and the other layers use binary data in the time domain. This can achieve a balance between accuracy and computing resources. Lastly, we take the MNIST dataset as an example to test the above two models on the field-programmable gate array (FPGA) platform. The results show that the two models can be used as neural network acceleration units with low storage requirements and low power consumption for classification tasks under the condition that the accuracy decline is small. The joint design method in the time domain may further inspire other computing architectures. In addition, the design of Model II has certain reference significance for the design of more complex classification tasks.

Binarized neural network of diode array with high concordance to vector–matrix multiplication

“…neuromorphic) computing systems have achieved significant improvements in parallel computing and efficient data processing 1 – 5 . In particular, binarized neural networks (BNNs) have recently demonstrated their capabilities in image recognition applications 6 – 10 . The accelerators in the BNNs perform a matrix "multiply accumulate" (MAC) operation (i.e.…”

“…vector–matrix multiplication (VMM)) between the binarized weights and analog inputs 6 , 7 . The bitwise MAC operation enables extensive applicability to resource-constrained platforms, such as edge devices and mobile processors, promising a considerable reduction in memory (approximately 32 ×) and computation (approximately 2 ×) requirements compared with other neural networks 6 – 10 . Furthermore, it is still difficult for emerging synaptic devices to fully implement analog neural networks (analog input and analog weight) with nonlinear conductance changes and device variations 1 – 4 , 11 – 13 .…”

An Energy-Efficient Field-Programmable Gate Array Rapid Implementation of a Structural Health Monitoring System

“…They significantly reduce the hardware requirements with a minimal impact on the accuracy [8,9]. An interesting, practical use of FPGA systems to implement CNN is presented in the works [10][11][12][13]. Several algorithmic optimization strategies used in FPGA hardware for CNNs are discussed, and a few of neural network FPGA-based accelerators architectures are presented.…”

Efficient Two-Stage Max-Pooling Engines for an FPGA-Based Convolutional Neural Network

“…Among these options, FPGAs have gained popularity for implementing CNNs in embedded systems primarily due to their ability to perform convolution operations in parallel with high energy efficiency. Compared to GPUs, FPGAs offer higher energy efficiency, making them an attractive choice for resource-constrained embedded systems [4][5][6][7].…”