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Achieving ideal accuracies in analog neuromorphic computing using periodic carry

Digest of Technical Papers - Symposium on VLSI Technology

Agarwal, Sapan A.; Jacobs-Gedrim, Robin B.; Hsia, Alexander W.; Hughart, David R.; Fuller, Elliot J.; Talin, A.A.; James, Conrad D.; Plimpton, Steven J.; Marinella, Matthew J.

Analog resistive memories promise to reduce the energy of neural networks by orders of magnitude. However, the write variability and write nonlinearity of current devices prevent neural networks from training to high accuracy. We present a novel periodic carry method that uses a positional number system to overcome this while maintaining the benefit of parallel analog matrix operations. We demonstrate how noisy, nonlinear TaOx devices that could only train to 80% accuracy on MNIST, can now reach 97% accuracy, only 1% away from an ideal numeric accuracy of 98%. On a file type dataset, the TaOx devices achieve ideal numeric accuracy. In addition, low noise, linear Li1-xCoO2 devices train to ideal numeric accuracies using periodic carry on both datasets.

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TYPE Conference Poster YEAR 2017
ScopusOSTIDOI

Designing an analog crossbar based neuromorphic accelerator

2017 5th Berkeley Symposium on Energy Efficient Electronic Systems, E3S 2017 - Proceedings

Agarwal, Sapan A.; Hsia, Alexander W.; Jacobs-Gedrim, Robin B.; Hughart, David R.; Plimpton, Steven J.; James, Conrad D.; Marinella, Matthew J.

Resistive memory crossbars can dramatically reduce the energy required to perform computations in neural algorithms by three orders of magnitude when compared to an optimized digital ASIC [1]. For data intensive applications, the computational energy is dominated by moving data between the processor, SRAM, and DRAM. Analog crossbars overcome this by allowing data to be processed directly at each memory element. Analog crossbars accelerate three key operations that are the bulk of the computation in a neural network as illustrated in Fig 1: vector matrix multiplies (VMM), matrix vector multiplies (MVM), and outer product rank 1 updates (OPU)[2]. For an NxN crossbar the energy for each operation scales as the number of memory elements O(N2) [2]. This is because the crossbar performs its entire computation in one step, charging all the capacitances only once. Thus the CV2 energy of the array scales as array size. This fundamentally better than trying to read or write a digital memory. Each row of any NxN digital memory must be accessed one at a time, resulting in N columns of length O(N) being charged N times, requiring O(N3) energy to read a digital memory. Thus an analog crossbar has a fundamental O(N) energy scaling advantage over a digital system. Furthermore, if the read operation is done at low voltage and is therefore noise limited, the read energy can even be independent of the crossbar size, O(1) [2].

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TYPE Conference Poster YEAR 2017
ScopusOSTIDOI
14 Results
14 Results