2 citations · 5 across the 14 of their papers we have counts for
6 papers · 1 filter
CKTSO: High-Performance Parallel Sparse Linear Solver for General Circuit Simulations
Xiaoming Chen
This paper introduces CKTSO (abbreviation of "circuit solver"), a novel sparse linear solver specially designed for the simulation program with integrated circuit emphasis (SPICE).…
PIMCOMP: An End-to-End DNN Compiler for Processing-In-Memory Accelerators
Xiaotian Sun, Xinyu Wang, Wanqian Li +2
Various processing-in-memory (PIM) accelerators based on various devices, micro-architectures, and interfaces have been proposed to accelerate deep neural networks (DNNs). How to d…
BitQ: Tailoring Block Floating Point Precision for Improved DNN Efficiency on Resource-Constrained Devices
Yongqi Xu, Yujian Lee, Gao Yi +6
Deep neural networks (DNNs) are powerful for cognitive tasks such as image classification, object detection, and scene segmentation. One drawback however is the significant high co…
Older and Wiser: The Marriage of Device Aging and Intellectual Property Protection of Deep Neural Networks
Ning Lin, Shaocong Wang, Yue Zhang +6
Deep neural networks (DNNs), such as the widely-used GPT-3 with billions of parameters, are often kept secret due to high training costs and privacy concerns surrounding the data u…
PIMSYN: Synthesizing Processing-in-memory CNN Accelerators
Wanqian Li, Xiaotian Sun, Xinyu Wang +3
Processing-in-memory architectures have been regarded as a promising solution for CNN acceleration. Existing PIM accelerator designs rely heavily on the experience of experts and r…
PIMSIM-NN: An ISA-based Simulation Framework for Processing-in-Memory Accelerators
Xinyu Wang, Xiaotian Sun, Yinhe Han +1
Processing-in-memory (PIM) has shown extraordinary potential in accelerating neural networks. To evaluate the performance of PIM accelerators, we present an ISA-based simulation fr…