Details
Presenter(s)
![Wooyoung Jo Headshot](https://confcats-catavault.s3.amazonaws.com/CATAVault/ieeecass/master/files/styles/cc_user_photo/s3/user-pictures/17341.jpg?h=ad518777&itok=-MvNjH4C)
Display Name
Wooyoung Jo
- Affiliation
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AffiliationKorea Advanced Institute of Science and Technology
- Country
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CountrySouth Korea
Abstract
A Mixed-mode Computing-in memory (CIM) processor for supporting mixed-precision Deep Neural Network (DNN) processing is proposed. Previous CIM processors cannot exploit energy-efficient computation of mixed-precision DNNs. This paper proposes an energy-efficient mixed-mode CIM processor with two key features: 1) Mixed-Mode Mixed-precision CIM (M3-CIM) which achieves 55.46% energy efficiency improvement. 2) Digital-CIM for In-memory MAC for increasing the throughput of M3-CIM. The proposed CIM processor was simulated in 28nm CMOS technology and occupies 1.96 mm2. It achieves a state-of-the-art energy efficiency of 161.6 TOPS/W with 72.8% accuracy at ImageNet (ResNet50).