A 22nm 4Mb STT-MRAM Data-Encrypted Near-Memory Computation Macro with a 192GB/s Read-and-Decryption Bandwidth and 25.1-55.1TOPS/W 8b MAC for AI Operations

2022 IEEE International Solid- State Circuits Conference (ISSCC)（2022）

引用 31|浏览21

暂无评分

摘要

Nonvolatile computing-in-memory (nvCIM) [1]–[4] is ideal for battery-powered tiny artificial intelligence (AI) edge devices that require nonvolatile data storage and low system-level power consumption. Data encryption/decryption (data-ED) is also required to prevent access to the neural network (NN) model weights and the personalized data used to improve inference accuracy. This paper presents an ...

查看译文

关键词

Power demand,Nonvolatile memory,Memory management,Bandwidth,Artificial neural networks,Voltage,Energy efficiency

AI 理解论文

溯源树

样例

生成溯源树，研究论文发展脉络

Chat Paper

正在生成论文摘要