1 citations · 1 across the 4 of their papers we have counts for
4 papers
Benchmarking spiking neural networks across sensing modalities on edge devices
Xin Du, Di Yu, Changze Lv +12
Edge computing systems need to support diverse sensing workloads under tight energy and memory constraints, thereby motivating deployment-aware model selection. Spiking neural netw…
Edge Intelligence with Spiking Neural Networks
Shuiguang Deng, Di Yu, Changze Lv +10
The convergence of artificial intelligence and edge computing has spurred growing interest in enabling intelligent services directly on resource-constrained devices. While traditio…
SpecInF: Exploiting Idle GPU Resources in Distributed DL Training via Speculative Inference Filling
Cunchi Lv, Xiao Shi, Dong Liang +2
Deep Learning (DL), especially with Large Language Models (LLMs), brings benefits to various areas. However, DL training systems usually yield prominent idling GPU resources due to…
Dilu: Enabling GPU Resourcing-on-Demand for Serverless DL Serving via Introspective Elasticity
Cunchi Lv, Xiao Shi, Zhengyu Lei +4
Serverless computing, with its ease of management, auto-scaling, and cost-effectiveness, is widely adopted by deep learning (DL) applications. DL workloads, especially with large l…