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Digital In-Memory Compute for Machine Learning Applications With Input and Model Security

delete2025-01-01
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PRE
AI
M
Maitreyi Ashok
S
Saurav Maji
X
Xin Zhang
J
John Cohn
A
Anantha P. Chandrakasan
DOI：10.1109/JSSC.2025.3534753delete
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摘要

摘要

En 中文
内存内计算（IMC）架构能够在满足许多机器学习应用所需的高精度和高准确性的同时，通过高度的数据复用和并行性来降低能耗。然而，一个常被忽视的参数是安全性，这对于保持加速器的隐私和完整性是必要的。在本工作中，我们提出了一种能够抵御两种窃听攻击的IMC宏设计，包括被动物理旁路通道和内存总线探测。这是通过安全计算（无需随机位）、使用轻量级密码进行本地模型解密以及利用现有IMC电路生成密钥来实现的。这些贡献为所有实际攻击者（超过100万个样本）提供了旁路通道安全性，同时在8.1 TOPS/W的能效下运行，且对神经网络精度没有任何影响。
Keyword:
In-memory compute (IMC)
machine learning
physically unclonable functions
side-channel security

期刊

I
IEEE Journal of Solid-State Circuits
IF:
5.6
论文数:
916
被引数:
2.7W

机构

M
Massachusetts Institute of Technology
学者数:
2.4K
论文数: 1.1K
被引数: 8
M
mit-ibm watson ai laboratory
学者数:
1
论文数: 1
被引数: 0
I
ibm t. j. watson research center
学者数:
15
论文数: 13
被引数: 0
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