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IGZO-Based Compute Cell for Analog In-Memory Computing—DTCO Analysis to Enable Ultralow-Power AI at Edge
- Source :
- IEEE Transactions on Electron Devices. 67:4616-4620
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- We propose, for the first time, an indium gallium zinc oxide (IGZO)-based 2T1C compute cell (IGZO-cell) for analog in-memory computing. To assess the impact of an IGZO-cell-based array including the periphery on power and accuracy, a PyTorch framework was developed to analytically modeled analog components. The results are reported for a ResNet20 network on the Canadian Institute For Advanced Research-10 (CIFAR-10) benchmark. The state-of-the-art energy efficiency of 15 peta operations per second (POPS)/W including the periphery is achieved by using our proposed IGZO-cell with CMOS compatibility. Finally, it is shown that, with a properly trained neural network model, there is no degradation of test accuracy with 10% device to device variability for the IGZO devices.
- Subjects :
- 010302 applied physics
Indium gallium zinc oxide
Artificial neural network
Computer science
01 natural sciences
Electronic, Optical and Magnetic Materials
Power (physics)
In-Memory Processing
0103 physical sciences
Benchmark (computing)
Electronic engineering
Field-effect transistor
Enhanced Data Rates for GSM Evolution
Electrical and Electronic Engineering
Efficient energy use
Subjects
Details
- ISSN :
- 15579646 and 00189383
- Volume :
- 67
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Electron Devices
- Accession number :
- edsair.doi...........6bdd3ace0c527f208a22365edce5cd77