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This letter discusses mixed-mode magneto tunneling junction (m-MTJ)-based restricted Boltzmann machine (RBM). RBMs are unsupervised learning models, suitable for extracting features from high-dimensional data. The m-MTJ is actuated by the simultaneous actions of voltage-controlled magnetic anisotropy and voltage-controlled spin-transfer torque, where the switching of the free-layer is probabilistic and can be controlled by the two. Using m-MTJ-based activation functions, we present a novel low area/power RBM. We discuss online learning of the presented implementation to negate process variability. For MNIST hand-written dataset, the design achieves ~96% accuracy under expected variability in various components.

In this work, we introduce bitcell array-based support parameters to improve the prediction accuracy of SRAM-based binarized neural network (SRAM-BNN). Our approach enhances the training weight space of SRAM-BNN while requiring minimal overheads to a typical design. More flexibility of the weight space leads to higher prediction accuracy in our design. We adapt row digital-to-analog (DAC) converter, and computing flow in SRAM-BNN for bitcell array-based weight supports. Using the discussed interventions, our scheme also allows a dynamic trade-off of accuracy against energy to address dynamic energy constraints in typical real-time applications. Our approach reduces classification error in MNIST from 1.4% to 0.91%. To reduce the power overheads, we propose a dynamic drop out of support parameters, which also reduces the processing energy of the in-SRAM weight-input product Our architecture can dropout 52% of the bitcell array-based support parameters with only minimal accuracy degradation. We also characterize our design under varying degrees of process variability in the transistors.

We present a mixed-mode magneto tunneling junction (m-MTJ)-based Deep Belief Network (DBN). DBNs are unsupervised learning models, suitable for recognition and clustering. m-MTJ is a three-terminal magnetic device with probabilistic free layer switching controlled by the simultaneous actions of voltage-controlled magnetic anisotropy and spin-transfer torque. While DBNs achieve high prediction accuracy even with highly imprecise single-bit weights, the key complexity lies in their activation functions which are stochastic. Using an m-MTJ, we present a novel low area/power DBN neuron with stochastic activation function. We discuss an in-memory computing architecture that allows forward and backward flow of learning dynamics and online learning. Our design achieves ~88.80% accuracy for digit recognition in MNIST even under the worst case variability in nanoscaled m-MTJs.

This work discusses a CMOS-based implementation of low power speaker identification (SI) using Gaussian mixture models (GMMs). Conventionally, GMM-based SI relies on repeated access to log-add look-up table (LUT). With increasing dimensional of speaker models $\mathbf{and}/\mathbf{or}$ number of speakers in the database, accesses to the LUT dominate the overall energy expense for SI. In this work, we discuss piece-wise linear approximations to GMM model that eliminate LUT accesses, thereby limiting model parameter storage in the register files alone, while incurring a minimal accuracy drop. We evaluate our scheme on TIMIT corpus where for text-independent SI and with a two-second test speech, our scheme achieves more than $90\%$ accuracy across test-sets. We discuss the detailed architecture of the control unit, datapath, and key modules in our scheme. Compared to an equivalent design that requires LUT accesses in $\mathrm{off}$ -chip memories, our scheme limits power dissipation for SI to $\sim 600\ \mu \mathbf{W}$ and consumes $4\times$ less energy