Publications

Designing and modeling analog neural network training accelerators

Agarwal, Sapan A.; Jacobs-Gedrim, Robin B.; Bennett, Christopher H.; Hsia, Alexander W.; Adee, Shane M.; Hughart, David R.; Fuller, Elliot J.; Li, Yiyang; Talin, A.A.; Marinella, Matthew J.

Analog crossbars have the potential to reduce the energy and latency required to train a neural network by three orders of magnitude when compared to an optimized digital ASIC. The crossbar simulator, CrossSim, can be used to model device nonidealities and determine what device properties are needed to create an accurate neural network accelerator. Experimentally measured device statistics are used to simulate neural network training accuracy and compare different classes of devices including TaOx ReRAM, Lir-Co-Oz devices, and conventional floating gate SONOS memories. A technique called 'Periodic Carry' can overcomes device nonidealities by using a positional number system while maintaining the benefit of parallel analog matrix operations.