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Posts by Collection
portfolio
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publications
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.
Older and Wise: The Marriage of Device Aging and Intellectual Property Protection of DNNs
Published in DAC'24, 2024
Previous approaches to securing DNNs typically require expensive circuit redesign, resulting in additional overheads such as increased area, energy consumption, and latency. To address these issues, we propose a novel hardware-software co-design for DNN protection that leverages the inherent aging characteristics of circuits to provide effect protection.
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.
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.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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