Electrically-Tunable Stochasticity for Spin-based Neuromorphic Circuits: Self-Adjusting to Variation
This addresses energy-efficient neuromorphic circuit fabrication for AI hardware by mitigating variation impacts, though it is incremental as it builds on existing p-bit and DBN methods.
The paper tackled the problem of process variation sensitivity in stochastic Deep Belief Networks using nanomagnetic devices by optimizing the energy barrier of Magnetic Tunnel Junctions to adjust stochasticity, showing a stability factor change of 5 orders of magnitude on the MNIST dataset.
Energy-efficient methods are addressed for leveraging low energy barrier nanomagnetic devices within neuromorphic architectures. Using a Magnetoresistive Random Access Memory (MRAM) probabilistic device (p-bit) as the basis of neuronal structures in Deep Belief Networks (DBNs), the impact of reducing the Magnetic Tunnel Junction's (MTJ's) energy barrier is assessed and optimized for the resulting stochasticity present in the learning system. This can mitigate the process variation sensitivity of stochastic DBNs which encounter a sharp drop-off when energy barriers exceed near-zero kT. As evaluated for the MNIST dataset for energy barriers at near-zero kT to 2.0 kT in increments of 0.5 kT, it is shown that the stability factor changes by 5 orders of magnitude. The self-compensating circuit developed herein provides a compact, and low complexity approach to mitigating process variation impacts towards practical implementation and fabrication.