Reservoir Computing using Stochastic p-Bits
This work addresses the need for efficient hardware integration of temporal neural networks in applications like IoT, industrial controls, and self-driving vehicles, representing an incremental advancement in hardware design.
The authors tackled the challenge of implementing Reservoir Computing in hardware by proposing a general framework and a specific hardware unit using soft-magnets, spin-orbit materials, and CMOS transistors, enabling efficient non von-Neumann architectures for temporal neural networks.
We present a general hardware framework for building networks that directly implement Reservoir Computing, a popular software method for implementing and training Recurrent Neural Networks and are particularly suited for temporal inferencing and pattern recognition. We provide a specific example of a candidate hardware unit based on a combination of soft-magnets, spin-orbit materials and CMOS transistors that can implement these networks. Efficient non von-Neumann hardware implementation of reservoir computers can open up a pathway for integration of temporal Neural Networks in a wide variety of emerging systems such as Internet of Things (IoTs), industrial controls, bio- and photo-sensors, and self-driving automotives.