27 citations · 41 across the 7 of their papers we have counts for
7 papers
TabConv: Low-Computation CNN Inference via Table Lookups
Neelesh Gupta, Narayanan Kannan, Pengmiao Zhang +1
Convolutional Neural Networks (CNNs) have demonstrated remarkable ability throughout the field of computer vision. However, CNN inference requires a large number of arithmetic oper…
Attention, Distillation, and Tabularization: Towards Practical Neural Network-Based Prefetching
Pengmiao Zhang, Neelesh Gupta, Rajgopal Kannan +1
Attention-based Neural Networks (NN) have demonstrated their effectiveness in accurate memory access prediction, an essential step in data prefetching. However, the substantial com…
PaCKD: Pattern-Clustered Knowledge Distillation for Compressing Memory Access Prediction Models
Neelesh Gupta, Pengmiao Zhang, Rajgopal Kannan +1
Deep neural networks (DNNs) have proven to be effective models for accurate Memory Access Prediction (MAP), a critical task in mitigating memory latency through data prefetching. H…
ME-ViT: A Single-Load Memory-Efficient FPGA Accelerator for Vision Transformers
Kyle Marino, Pengmiao Zhang, Viktor Prasanna
Vision Transformers (ViTs) have emerged as a state-of-the-art solution for object classification tasks. However, their computational demands and high parameter count make them unsu…
Phases, Modalities, Temporal and Spatial Locality: Domain Specific ML Prefetcher for Accelerating Graph Analytics
Pengmiao Zhang, Rajgopal Kannan, Viktor K. Prasanna
Memory performance is a bottleneck in graph analytics acceleration. Existing Machine Learning (ML) prefetchers struggle with phase transitions and irregular memory accesses in grap…
TransforMAP: Transformer for Memory Access Prediction
Pengmiao Zhang, Ajitesh Srivastava, Anant V. Nori +2
Data Prefetching is a technique that can hide memory latency by fetching data before it is needed by a program. Prefetching relies on accurate memory access prediction, to which ta…
Fine-Grained Address Segmentation for Attention-Based Variable-Degree Prefetching
Pengmiao Zhang, Ajitesh Srivastava, Anant V. Nori +2
Machine learning algorithms have shown potential to improve prefetching performance by accurately predicting future memory accesses. Existing approaches are based on the modeling o…