activity
20212024
most citedComputing-In-Memory Neural Network Accelerators for Safety-Critical Systems: Can Small Device Variations Be Disastrous?

18 citations · 39 across the 11 of their papers we have counts for

collaborators

13 papers

cs.LG2024

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs

Ruiyang Qin, Pengyu Ren, Zheyu Yan +7

Large Language Models (LLMs) deployed on edge devices, known as edge LLMs, need to continuously fine-tune their model parameters from user-generated data under limited resource con…

cond-mat.mtrl-sci2024

Design of Programmable Temperature Platform and its Pyroelectrocatalytic applications

Xiechao Hu, Chengxi Hu, Tieyan Guo +4

The Si based TiO2 thin films were prepared via the combination both of Sol-Gel and Spin-Coating method. The films were sintered at 850 degrees Celsius for half an hour, and the res…

cs.AR20243 cited

CAMASim: A Comprehensive Simulation Framework for Content-Addressable Memory based Accelerators

Mengyuan Li, Shiyi Liu, Mohammad Mehdi Sharifi +1

Content addressable memory (CAM) stands out as an efficient hardware solution for memory-intensive search operations by supporting parallel computation in memory. However, developi…

cs.AR20234 cited

SEE-MCAM: Scalable Multi-bit FeFET Content Addressable Memories for Energy Efficient Associative Search

Shengxi Shou, Che-Kai Liu, Sanggeon Yun +7

In this work, we propose SEE-MCAM, scalable and compact multi-bit CAM (MCAM) designs that utilize the three-terminal ferroelectric FET (FeFET) as the proxy. By exploiting the multi…

cs.AR20231 cited

C4CAM: A Compiler for CAM-based In-memory Accelerators

Hamid Farzaneh, João Paulo Cardoso de Lima, Mengyuan Li +3

Machine learning and data analytics applications increasingly suffer from the high latency and energy consumption of conventional von Neumann architectures. Recently, several in-me…

cs.CR20231 cited

Privacy Preserving In-memory Computing Engine

Haoran Geng, Jianqiao Mo, Dayane Reis +5

Privacy has rapidly become a major concern/design consideration. Homomorphic Encryption (HE) and Garbled Circuits (GC) are privacy-preserving techniques that support computations o…