Design and evaluating performance of hardware accelerators for implementation of neural networks

Abstract

Neural networks have emerged as a powerful tool in machine learning and deep learning, showcasing remarkable capabilities in image recognition, natural language processing, and autonomous decision-making tasks. However, the execution of neural network models often requires substantial computational resources, leading to prolonged execution times. This computational burden hinders real-time deployment in resource-constrained environments. To address this challenge, hardware accelerators have emerged as a promising solution. Hardware accelerators are specialized computing units designed to efficiently execute specific computational tasks, offering significant performance improvements over traditional software-based implementations. In this paper, we propose a comprehensive evaluation framework for hardware accelerators in implementing neural networks. The framework will consider various aspects of accelerator performance, including execution time and resource utilization, and take into account the impact of different neural network architectures and hardware platforms. By evaluating the execution time and accuracy of standard neural networks compared to those with hardware accelerators performing in-memory computation of multiple convolution operations, we demonstrate the effectiveness of implementing neural networks using hardware accelerators. This framework will provide researchers and developers with a systematic approach to selecting and designing specialized computing units for neural network applications, enabling informed decision-making and improving overall system efficiency.