An exploration of efficient computing techniques for implementing deep neural network accelerators

Abstract

Modern artificial intelligence (AI) applications rely heavily on deep neural networks (DNNs) to perform complex computational tasks. Although DNNs deliver high accuracy, they do so at the cost of substantial power consumption and significant hardware resource utilization, making their edge implementation a major challenge. Edge devices such as Internet of Things (IoT) nodes, mobile platforms, and embedded systems operate under stringent constraints on power, memory, and computational capability. Consequently, deploying DNNs at the edge requires highly efficient computing architectures and optimization techniques that reduce energy consumption and hardware overhead while preserving acceptable levels of inference accuracy and realtime performance. This necessitates optimising DNNs to achieve optimal resource and power consumption while minimising accuracy degradation. Owing to the limited ability of traditional CPUs to efficiently process large volumes of data at high speed, modern DNN workloads increasingly require hardware acceleration. To meet these growing computational demands, a wide range of hardware acceleration platforms have been developed to offload compute-intensive operations from general-purpose CPUs. These platforms—including Field-Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), and Graphics Processing Units (GPUs)—offer higher computational throughput and improved energy efficiency. Consequently, efficient utilization and architectural design of such accelerators are critical for enabling scalable, high-performance DNN processing in complex and data-intensive applications.