7 citations · 22 across the 7 of their papers we have counts for
10 papers
Layer Freezing & Data Sieving: Missing Pieces of a Generic Framework for Sparse Training
Geng Yuan, Yanyu Li, Sheng Li +5
Recently, sparse training has emerged as a promising paradigm for efficient deep learning on edge devices. The current research mainly devotes efforts to reducing training costs by…
PIM-QAT: Neural Network Quantization for Processing-In-Memory (PIM) Systems
Qing Jin, Zhiyu Chen, Jian Ren +3
Processing-in-memory (PIM), an increasingly studied neuromorphic hardware, promises orders of energy and throughput improvements for deep learning inference. Leveraging the massive…
Neural Network-based OFDM Receiver for Resource Constrained IoT Devices
Nasim Soltani, Hai Cheng, Mauro Belgiovine +10
Orthogonal Frequency Division Multiplexing (OFDM)-based waveforms are used for communication links in many current and emerging Internet of Things (IoT) applications, including the…
AirNN: Neural Networks with Over-the-Air Convolution via Reconfigurable Intelligent Surfaces
Sara Garcia Sanchez, Guillem Reus Muns, Carlos Bocanegra +6
Over-the-air analog computation allows offloading computation to the wireless environment through carefully constructed transmitted signals. In this paper, we design and implement…
ILMPQ : An Intra-Layer Multi-Precision Deep Neural Network Quantization framework for FPGA
Sung-En Chang, Yanyu Li, Mengshu Sun +2
This work targets the commonly used FPGA (field-programmable gate array) devices as the hardware platform for DNN edge computing. We focus on DNN quantization as the main model com…
RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions
Sung-En Chang, Yanyu Li, Mengshu Sun +4
This work proposes a novel Deep Neural Network (DNN) quantization framework, namely RMSMP, with a Row-wise Mixed-Scheme and Multi-Precision approach. Specifically, this is the firs…