2 citations · 3 across the 4 of their papers we have counts for
4 papers
HLSmith: An Expert-Guided Agentic Framework for C/C++-to-HLS Translation
Yuebo Luo, Ahmad Sedigh Baroughi, Philip Stachura +4
Application-specific FPGA accelerators offer substantial performance and energy-efficiency gains across many application domains, but developing them is costly, often requiring mon…
Perturbation-efficient Zeroth-order Optimization for Hardware-friendly On-device Training
Qitao Tan, Sung-En Chang, Rui Xia +10
Zeroth-order (ZO) optimization is an emerging deep neural network (DNN) training paradigm that offers computational simplicity and memory savings. However, this seemingly promising…
Towards Accurate and Efficient Sub-8-Bit Integer Training
Wenjin Guo, Donglai Liu, Weiying Xie +7
Neural network training is a memory- and compute-intensive task. Quantization, which enables low-bitwidth formats in training, can significantly mitigate the workload. To reduce qu…
SASA: A Scalable and Automatic Stencil Acceleration Framework for Optimized Hybrid Spatial and Temporal Parallelism on HBM-based FPGAs
Xingyu Tian, Zhifan Ye, Alec Lu +3
Stencil computation is one of the fundamental computing patterns in many application domains such as scientific computing and image processing. While there are promising studies th…