77 citations · 126 across the 12 of their papers we have counts for
6 papers · 1 filter
From Human Guidance to Autonomy: Agent Skill System for End-to-End LLM Deployment on Spatial NPUs
Jiajie Li, Erwei Wang, Zhiru Zhang +1
Spatial neural processing units (NPUs) provide an energy-efficient platform for edge LLM inference, but efficiently deploying an LLM end-to-end on such hardware remains labor-inten…
Logic Shrinkage: Learned FPGA Netlist Sparsity for Efficient Neural Network Inference
Erwei Wang, James J. Davis, Georgios-Ilias Stavrou +3
FPGA-specific DNN architectures using the native LUTs as independently trainable inference operators have been shown to achieve favorable area-accuracy and energy-accuracy tradeoff…
Accelerating Recurrent Neural Networks for Gravitational Wave Experiments
Zhiqiang Que, Erwei Wang, Umar Marikar +10
This paper presents novel reconfigurable architectures for reducing the latency of recurrent neural networks (RNNs) that are used for detecting gravitational waves. Gravitational i…
Enabling Binary Neural Network Training on the Edge
Erwei Wang, James J. Davis, Daniele Moro +6
The ever-growing computational demands of increasingly complex machine learning models frequently necessitate the use of powerful cloud-based infrastructure for their training. Bin…
LUTNet: Learning FPGA Configurations for Highly Efficient Neural Network Inference
Erwei Wang, James J. Davis, Peter Y. K. Cheung +1
Research has shown that deep neural networks contain significant redundancy, and thus that high classification accuracy can be achieved even when weights and activations are quanti…
LUTNet: Rethinking Inference in FPGA Soft Logic
Erwei Wang, James J. Davis, Peter Y. K. Cheung +1
Research has shown that deep neural networks contain significant redundancy, and that high classification accuracies can be achieved even when weights and activations are quantised…