Neuromorphic Hardware Accelerator for SNN Inference based on STTRAM Crossbar Arrays In this paper, we propose a Spin Transfer Torque RAM (STTRAM) based neurosynaptic core to implement a hardware accelerator for Spiking Neural Networks (SNNs), which mimic the timebased signal encoding and processing mechanisms of the human brain. The computational core consists of a crossbar array of nonvolatile STTRAMs, readwrite peripheral circuits, and digital logic for the spiking neurons. Intercore communication is realized through onchip routing network by sendingreceiving spike packets. Unlike prior works that use multilevel states of nonvolatile memory (NVM) devices for the synaptic weights, we use the technologicallymature STTRAM devices for binary data storage. The design studies are conducted using a compact model for STTRAM devices, tuned to capture the stateoftheart experimental results. Our design avoids the need for expensive ADCs and DACs, enabling instantiation of large NVM arrays for our core. We show that the STTRAM based neurosynaptic core designed in 28 nm technology node has approximately 6xc3x97 higher throughput per unit Watt and unit area than an equivalent SRAM based design. Our design also achieves xe2x88xbc 2xc3x97 higher performance per Watt compared to other memristive neural network accelerator designs in the literature.

Nonfilamentary nonvolatile memory elements as synapses in neuromorphic systems Crossbar arrays of nonvolatile memory (NVM) devices represent one possible path for implementing highly energyefficient neuromorphic computing systems. For Deep Neural Networks (DNN), where information can be encoded as analog voltage and current levels, such arrays can represent matrices of synaptic weights, implementing the matrixvector multiplication needed for algorithms such as backpropagation in a massivelyparallel fashion. Previous research demonstrated a largescale hardwaresoftware implementation based on phasechange memories and analyzed the potential speed and power advantages over GPUbased training. In this proceeding we will discuss extensions of this work leveraging a different class of memory elements. Using the concept of jumptables we simulate the impact of real conductance response of nonfilamentary resistive devices based on P r_0._3Ca_0.7 Mn O_3 (PCMO). With the same approach as of 1, we simulate a threelayer neural network with training accuracy u003e90% on the MNIST dataset. The higher ONOFF conductance ratio of improved AlMoPCMO devices together with new programming strategies can lead to further accuracy improvement. Finally, we show that the bidirectional programming of AlMoPCMO can be used to implement highdensity neuromorphic systems with a single conductance per synapse, at only a slight degradation to accuracy.