A 22nm 104.5TOPS/W μ-NMC-Δ-IMC Heterogeneous STT-MRAM CIM Macro for Noise-Tolerant Bayesian Neural Networks
National Tsing Hua University and TSMC present an STT-MRAM CIM macro for noise-tolerant Bayesian neural networks with a heterogeneous in- and near-memory MAC structure. The 22nm macro achieves 104.5TOPS/W with a 0.03% accuracy loss for CIFAR-100. authors: D-Q. You, W-S. Khwa, B. Zhang, F-Y. Chen, A. Lee, Y-C. Hung, Y-M. Li, Y-H. Wang, C-C. Lo, R-S. Liu, K-T. Tang, C-C. Hsieh, Y-D. Chih, T-Y. J. Chang, M-F. Chang, National Tsing Hua University, Hsinchu, Taiwan, TSMC Corporate Research, Hsinchu, Taiwan, TSMC Corporate Research, San Jose, CA, TSMC, Hsinchu, Taiwan
Artificial Intelligence
Artificial Intelligence
Since the last decade, we have been witnessing a steep rise of Artificial Intelligence (AI) as an alternative computing paradigm. Although the idea has been around since 1950s, AI needed progress in algorithms, capable hardware, and sufficiently large training data to become a practical and powerful tool. Progress in computing hardware has been a key ingredient for the AI renaissance and will remain increasingly critical to realize future AI applications.
We are particularly well-positioned to supply the most advanced AI hardware to our customers thanks to our leading-edge logic, memory, and packaging technologies. We have established a research pipeline for technology to enable leading-edge AI devices, circuits, and systems for decades to come. Near- and in-memory computing, embedded non-volatile memory technologies, 3D integration, and error-resilient computing are amongst our specific AI hardware research areas. Our in-house research is complemented by strong academic and governmental partnerships, which allow us to interact with and influence leading AI researchers around the world.
