Design of a Robust Memristive Spiking Neuromorphic System with Unsupervised Learning in Hardware

Abstract

Spiking neural networks (SNN) offer a power efficient, biologically plausible learning paradigm by encoding information into spikes. The discovery of the memristor has accelerated the progress of spiking neuromorphic systems, as the intrinsic plasticity of the device makes it an ideal candidate to mimic a biological synapse. Despite providing a nanoscale form factor, non-volatility, and low-power operation, memristors suffer from device-level non-idealities, which impact system-level performance. To address these issues, this article presents a memristive crossbar-based neuromorphic system using unsupervised learning with twin-memristor synapses, fully digital pulse width modulated spike-timing-dependent plasticity, and homeostasis neurons. The implemented single-layer SNN was applied to a pattern-recognition task of classifying handwritten-digits. The performance of the system was analyzed by varying design parameters such as number of training epochs, neurons, and capacitors. Furthermore, the impact of memristor device non-idealities, such as device-switching mismatch, aging, failure, and process variations, were investigated and the resilience of the proposed system was demonstrated.

A Single Chip SPAD Based Vision Sensing System With Integrated Memristive Spiking Neuromorphic Processing

“…The mapping allows the SPAD sensor to be compatible with the implemented spiking neuromorphic processor in this work. The spiking neuromorphic system includes a spike generation scheme based on our prior work [31], wherein the pixel intensities range from -4 to +4, such that higher pixel intensity magnitude generates a spike closer to the center of the time frame. Positive and negative intensity pixels are placed before and after the center of the time frame, respectively.…”

“…The timing of the synchronized SPAD event and TCS [31] is illustrated in Fig. Simulation result of the proposed on-chip interfacing circuit.…”

“…The memristive spiking neuromorphic system shown in Fig. 14 is implemented using input neurons, twin memristors synapses, and homeostasis enabled integrate and fire output neurons based on prior work [31]. A single-layer spiking neural network is then implemented using these building blocks and a twin memristive crossbar to process the temporally coded spikes generated by the proposed on-chip interface from the output events of the SPAD-based vision sensor.…”

“…The output neuron illustrated in Fig. 14 is based on the Integrate And Fire (IAF) neurons described in [31] in which the integrator accumulates input current and the comparator compares the accumulated voltage against a threshold. The accumulation in output neuron, 𝑉 𝑎𝑐𝑐𝑢𝑚 , is defined as…”

Memristive devices based hardware for unlabeled data processing

“…Long-term plasticity includes long-term potentiation (LTP) and longterm depression (LTD), spike timing dependent plasticity (STDP), and spike rate dependent plasticity (SRDP), while short-term plasticity contains short-term potentiation (STP), paired-pulsed facilitation (PPF) and depression (PPD). They are originally discovered in neuroscience and thought to be the underlying mechanisms for complex learning and memory functions in the human brain [111].…”