22 citations · 34 across the 6 of their papers we have counts for
6 papers
COSM: A Cooperative Scheduling Framework for Concurrent PIM and CPU Execution on Mobile Devices
Yilong Zhao, Fangxin Liu, Onur Mutlu +4
The development of on-device large language models (LLMs) is driven by the need for privacy and fast response times. Energy-intensive data transfer on mobile devices makes Processi…
PUSHtap: PIM-based In-Memory HTAP with Unified Data Storage Format
Yilong Zhao, Mingyu Gao, Huanchen Zhang +5
Hybrid transaction/analytical processing (HTAP) is an emerging database paradigm that supports both online transaction processing (OLTP) and online analytical processing (OLAP) wor…
RePAST: A ReRAM-based PIM Accelerator for Second-order Training of DNN
Yilong Zhao, Li Jiang, Mingyu Gao +6
The second-order training methods can converge much faster than first-order optimizers in DNN training. This is because the second-order training utilizes the inversion of the seco…
Neural-PIM: Efficient Processing-In-Memory with Neural Approximation of Peripherals
Weidong Cao, Yilong Zhao, Adith Boloor +3
Processing-in-memory (PIM) architectures have demonstrated great potential in accelerating numerous deep learning tasks. Particularly, resistive random-access memory (RRAM) devices…
SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network
Fangxin Liu, Wenbo Zhao, Yilong Zhao +6
Resistive Random-Access-Memory (ReRAM) crossbar is a promising technique for deep neural network (DNN) accelerators, thanks to its in-memory and in-situ analog computing abilities…
An Ultra-Efficient Memristor-Based DNN Framework with Structured Weight Pruning and Quantization Using ADMM
Geng Yuan, Xiaolong Ma, Caiwen Ding +7
The high computation and memory storage of large deep neural networks (DNNs) models pose intensive challenges to the conventional Von-Neumann architecture, incurring substantial da…