Wong, Edwin Kwun Hang

Wong, Edwin Kwun Hang

MPhil Research Student @HKU, Interest: neuromorphic computing, memristive device, security

Publications

Journal Articles

Resistive memory-based zero-shot liquid state machine for multimodal event data learning

Published in Nature Computational Science, 2025

Replicating the human brain in neuromorphic hardware presents both hardware and software challenges. We propose a hardware–software co-design on a 40 nm 256 kB in-memory computing macro that physically integrates a fixed and random liquid state machine SNN encoder with trainable artificial neural network projections. We showcase the zero-shot learning of multimodal events on the N-MNIST and N-TIDIGITS datasets, including visual and audio data association, as well as neural and visual data alignment for brain–machine interfaces.

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In-memory and In-Sensor Reservoir Computing with Memristive Devices

Published in APL Machine Learning, 2024

Reservoir computing enables real-time edge learning thanks to its brain-inspired dynamic system with massive training complexity reduction. From this perspective, we survey recent advancements in in-memory/in-sensor reservoir computing, including algorithm designs, material and device development, and downstream applications in classification and regression problems, and discuss challenges and opportunities ahead in this emerging field.

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Conference Papers

SNNGX: Securing Spiking Neural Networks with Genetic XOR Encryption on RRAM-based Neuromorphic Accelerator

Published in ICCAD'24, 2024

There is a considerable risk from malicious attempts to extract white-box information (i.e., weights) from Spiking Neural Networks (SNNs) on neuromorphic accelerator since attackers could exploit well-trained SNNs from emerging NVM cells for profit, as well as causing white-box adversarial concerns. In this paper, we present a novel secure software-hardware co-designed RRAM-based neuromorphic accelerator for protecting the IP of SNNs.

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