Work

Enhanced convolutional neural network accelerators with memory optimization for routing applications

Year: 2025

Type: article

Abstract: Currently, Convolutional Neural Networks (CNN) accelerators find application in various digital domains, each highlighting memory utilization as a significant concern leading to system degradation. In response, our present work focuses on optimizing the memory usage of CNN through a strategic approach. The resulting system is coined as the Memory Optimized Zebra CNN (MOZC). In the initial stage, the CNN accelerator is constructed with optimized features, specifically addressing the network routing function. In this context, our approach draws inspiration from zebras, aiming to identify the shortest path between network nodes. The Field-Programmable-Gate-Arrays (FPGA) are employed for evaluating MOZC performance, considering parameters such as lookup table (LUT), Flip-Flop (FF), memory utilization, power consumption, Digital-Signal Processing (DSP), and Giga-Operations-Per-Second per watt (GOPS/W). Additionally, key parameters like data delivery and Throughput assess routing and data transmission robustness. Video data is utilized to determine routing efficiency, and the achieved highest GOPS/W is recorded as 30.43, marking a substantial improvement over conventional CNN accelerators.

Source: PLoS ONE

DOI: 10.1371/journal.pone.0320813

Authors: Srikanth Prasad Nallabelli, Sundar Sampath

Institution: Vellore Institute of Technology University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,382