In this paper, we validate and calibrate the prediction of NeuroSim against a 40nm RRAM-based CIM macro post-layout simulations. First, the parameters of memory device and CMOS transistor are extracted from the TSMC's PDK and employed on the NeuroSim settings; the peripheral modules and ...
et al. 24.1 A 1Mb multibit ReRAM computing-in-memory macro with 14.6ns parallel MAC computing time for CNN based AI edge processors. In 2019 IEEE International Solid-State Circuits Conference (ISSCC) Digest of Technical Papers 388–390 (IEEE, 2019). Chen, W.-H. et al. CMOS-integrated ...
A compute-in-memory chip based on resistive random-access memory. Nature 608, 504–512 (2022). Google Scholar Huo, Q. et al. A computing-in-memory macro based on three-dimensional resistive random-access memory. Nat. Electron. 5, 469–477 (2022). Google Scholar Zhang, W. et al. ...
et al. A 1Mb multibit ReRAM computing-in-memory macro with 14.6 ns parallel MAC computing time for CNN-based AI edge processors. In IEEE International Solid-State Circuits Conference (ISSCC) Digest of Technical Papers 388–390 (IEEE, 2019). Xue, C.-X. et al. A 22 nm 2 Mb ...
This paper demonstrates the first Monolithic 3D+-IC based Compute-in-Memory (CiM) Macro performing massively parallel beyond-Boolean operations targeting database and machine learning (ML) applications. The proposed CiM technique supports data filtering, sorting, and sparse matrix-matrix multiplication (...
29.1 A 40nm 64Kb 56.67TOPS/W Read-Disturb-Tolerant Compute-in-Memory/Digital RRAM Macro with Active-Feedback-Based Read and In-Situ Write Verification 来自 科研支点 喜欢 0 阅读量: 1 作者:JH Yoon,M Chang,WS Khwa,YD Chih,MF Chang,A Raychowdhury ...
Si X et al (2020) A 28nm 64Kb 6T SRAM computing-in-memory macro with 8b MAC operation for AI edge chips. In: IEEE international solid-state circuits conference (ISSCC) Google Scholar Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: In...
Tutorial Sessions Support Learn how to make the most of the Source Page in Nsight Compute to quickly pinpoint and resolve bottlenecks in your CUDA kernels. Watch Understand how your multi-node CUDA workload is scaling across machines and how a GPU assembly instruction is moving through the pipel...
Memory workload analysis builds a visualization of memory transfer sizes and throughput on the profiled architecture, as well as a guide for improving performance. Heatmaps allow users to intuitively understand potential bottlenecks and under-utilizations in the memory pipeline. Detailed tables for eac...
29.1 A 40nm 64Kb 56.67TOPS/W Read-Disturb-Tolerant Compute-in-Memory/Digital RRAM Macro with Active-Feedback-Based Read and In-Situ Write Verification (referred to as compute-in-memory, or CIM) for a target algorithm-level inference-accuracy [2] -[8], (2) voltage-based RD with active ...