Revolutionizing Scratchpad Memory Utilization in Deep Learning Accelerators for Optimal Performance

Abstract

The efficiency of deep learning accelerators is heavily influenced by memory management strategies, particularly in the utilization of scratchpad memory, a high-speed cache used to store frequently accessed data. However, traditional memory management approaches often fail to fully exploit the potential of scratchpad memory, resulting in suboptimal performance and increased power consumption. This paper presents a novel methodology for revolutionizing scratchpad memory utilization in deep learning accelerators, combining dynamic memory allocation with an intelligent data locality optimization strategy. Our approach dynamically allocates scratchpad memory based on workload demands, while optimizing the placement of data to reduce unnecessary memory accesses and improve throughput. Experimental results on state-of-the-art models, such as ResNet and VGG, show up to 30% reduction in execution time and a 25% decrease in energy consumption compared to existing memory management techniques. These findings highlight the potential of the proposed method to enhance the efficiency and scalability of deep learning accelerators, offering significant improvements for large-scale, memory-bound workloads.

Keywords- Revolutionizing Scratchpad Memory Utilization in Deep Learning Accelerators for Optimal Performance