IGZO-Based Compute Cell for Analog In-Memory Computing—DTCO Analysis to Enable Ultralow-Power AI at Edge
D. Saito, Jonas Doevenspeck, Stefan Cosemans, H. Oh, Manu Perumkunnil, Ioannis A. Papistas, Attilio Belmonte, Nouredine Rassoul, Romain Delhougne, Gouri Sankar Kar, Peter Debacker, A. Mallik, Diederik Verkest, M. H. Na
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.