Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective

On metrics of density and power efficiency, neuromorphic technologies have the potential to surpass mainstream computing technologies in tasks where real-time functionality, adaptability, and autonomy are essential. While algorithmic advances in neuromorphic computing are proceeding successfully, the potential of memristors to improve neuromorphic computing have not yet born fruit, primarily because they are often used as a drop-in replacement to conventional memory. However, interdisciplinary approaches anchored in machine learning theory suggest that multifactor plasticity rules matching neural and synaptic dynamics to the device capabilities can take better advantage of memristor dynamics and its stochasticity. Furthermore, such plasticity rules generally show much higher performance than that of classical Spike Time Dependent Plasticity (STDP) rules. This chapter reviews the recent development in learning with spiking neural network models and their possible implementation with memristor-based hardware

Deep Liquid State Machines with Neural Plasticity and On-Device Learning

The Liquid State Machine (LSM) is a recurrent spiking neural network designed for efficient processing of spatio-temporal streams of information. LSMs have several inbuilt features such as robustness, fast training and inference speed, generalizability, continual learning (no catastrophic forgetting), and energy efficiency. These features make LSM’s an ideal network for deploying intelligence on-device. In general, single LSMs are unable to solve complex real-world tasks. Recent literature has shown emergence of hierarchical architectures to support temporal information processing over different time scales. However, these approaches do not typically investigate the optimum topology for communication between layers in the hierarchical network, or assume prior knowledge about the target problem and are not generalizable. In this thesis, a deep Liquid State Machine (deep-LSM) network architecture is proposed. The deep-LSM uses staggered reservoirs to process temporal information on multiple timescales. A key feature of this network is that neural plasticity and attention are embedded in the topology to bolster its performance for complex spatio-temporal tasks. An advantage of the deep-LSM is that it exploits the random projection native to the LSM as well as local plasticity mechanisms to optimize the data transfer between sequential layers. Both random projections and local plasticity mechanisms are ideal for on-device learning due to their low computational complexity and the absence of backpropagating error. The deep-LSM is deployed on a custom learning architecture with memristors to study the feasibility of on-device learning. The performance of the deep-LSM is demonstrated on speech recognition and seizure detection applications

Neuro-memristive Circuits for Edge Computing: A review

The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing

MEMSORN: Self-organization of an inhomogeneous memristive hardware for sequence learning

Learning is a fundamental component for creating intelligent machines. Biological intelligence orchestrates synaptic and neuronal learning at multiple time-scales to self-organize populations of neurons for solving complex tasks. Inspired by this, we design and experimentally demonstrate an adaptive hardware architecture Memristive Self-organizing Spiking Recurrent Neural Network (MEMSORN). MEMSORN incorporates resistive memory (RRAM) in its synapses and neurons which configure their state based on Hebbian and Homeostatic plasticity respectively. For the first time, we derive these plasticity rules directly from the statistical measurements of our fabricated RRAM-based neurons and synapses. These “technologically plausible” learning rules exploit the intrinsic variability of the devices and improve the accuracy of the network on a sequence learning task by 30%. Finally, we compare the performance of MEMSORN to a fully-randomly set-up recurrent network on the same task, showing that self-organization improves the accuracy by more than 15%. This work demonstrates the importance of the device-circuit-algorithm co-design approach for implementing brain-inspired computing hardware

In-memory computing with emerging memory devices: Status and outlook

Supporting data for "In-memory computing with emerging memory devices: status and outlook", submitted to APL Machine Learning

Self-organization of an inhomogeneous memristive hardware for sequence learning

Learning is a fundamental component of creating intelligent machines. Biological intelligence orchestrates synaptic and neuronal learning at multiple time scales to self-organize populations of neurons for solving complex tasks. Inspired by this, we design and experimentally demonstrate an adaptive hardware architecture Memristive Self-organizing Spiking Recurrent Neural Network (MEMSORN). MEMSORN incorporates resistive memory (RRAM) in its synapses and neurons which configure their state based on Hebbian and Homeostatic plasticity respectively. For the first time, we derive these plasticity rules directly from the statistical measurements of our fabricated RRAM-based neurons and synapses. These "technologically plausible” learning rules exploit the intrinsic variability of the devices and improve the accuracy of the network on a sequence learning task by 30%. Finally, we compare the performance of MEMSORN to a fully-randomly-set-up spiking recurrent network on the same task, showing that self-organization improves the accuracy by more than 15%. This work demonstrates the importance of the device-circuit-algorithm co-design approach for implementing brain-inspired computing hardware