17 citations · 18 across the 7 of their papers we have counts for
5 papers · 1 filter
NIFA: Nonlinear IMC enhanced FPGA for efficient ML inference
Jiajun Hu, Ruthwik Reddy Sunketa, Lei Zhao +3
Recent FPGAs have improved deep learning (DL) inference efficiency through dedicated tensor blocks and in-BRAM computation. ReRAM-based analog in-memory computing (IMC) pushes effi…
Boosting FPGA Performance with Direct BRAM-DSP Paths
Jiajun Hu, Ruthwik Reddy Sunketa, Andrew Boutros +1
Efficient data movement between memory and compute units is a key performance bottleneck in modern FPGA designs, particularly for deep learning (DL) workloads. In typical FPGA arch…
CarbonSet: A Dataset to Analyze Trends and Benchmark the Sustainability of CPUs and GPUs
Jiajun Hu, Chetan Choppali Sudarshan, Vidya A. Chhabria +1
Over the years, the chip industry has consistently developed high-performance processors to address the increasing demands across diverse applications. However, the rapid expansion…
Field-Programmable Gate Array Architecture for Deep Learning: Survey & Future Directions
Andrew Boutros, Aman Arora, Vaughn Betz
Deep learning (DL) is becoming the cornerstone of numerous applications both in datacenters and at the edge. Specialized hardware is often necessary to meet the performance require…
GreenFPGA: Evaluating FPGAs as Environmentally Sustainable Computing Solutions
Chetan Choppali Sudarshan, Aman Arora, Vidya A. Chhabria
Growing global concerns about climate change highlight the need for environmentally sustainable computing. The ecological impact of computing, including operational and embodied, i…