9 citations · 14 across the 5 of their papers we have counts for
5 papers
H2PIPE: High throughput CNN Inference on FPGAs with High-Bandwidth Memory
Mario Doumet, Marius Stan, Mathew Hall +1
Convolutional Neural Networks (CNNs) combine large amounts of parallelizable computation with frequent memory access. Field Programmable Gate Arrays (FPGAs) can achieve low latency…
Microscaling Data Formats for Deep Learning
Bita Darvish Rouhani, Ritchie Zhao, Ankit More +30
Narrow bit-width data formats are key to reducing the computational and storage costs of modern deep learning applications. This paper evaluates Microscaling (MX) data formats that…
With Shared Microexponents, A Little Shifting Goes a Long Way
Bita Rouhani, Ritchie Zhao, Venmugil Elango +19
This paper introduces Block Data Representations (BDR), a framework for exploring and evaluating a wide spectrum of narrow-precision formats for deep learning. It enables compariso…
Neighbors From Hell: Voltage Attacks Against Deep Learning Accelerators on Multi-Tenant FPGAs
Andrew Boutros, Mathew Hall, Nicolas Papernot +1
Field-programmable gate arrays (FPGAs) are becoming widely used accelerators for a myriad of datacenter applications due to their flexibility and energy efficiency. Among these app…
HPIPE: Heterogeneous Layer-Pipelined and Sparse-Aware CNN Inference for FPGAs
Mathew Hall, Vaughn Betz
We present both a novel Convolutional Neural Network (CNN) accelerator architecture and a network compiler for FPGAs that outperforms all prior work. Instead of having generic proc…