The LSP Research Group at School of Computing, National University of Singapore is an inter-disciplinary research group working on multiple domains ranging from energy-efficient hardware systems and architecture for wearables, next-generation wearable sensing technologies, hardware for privacy-aware communication to hardware security against physical attacks.


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Sentry-NoC- A Statically-Scheduled NoC for Secure SoCs

The goal of Sentry-NoC is to enable side-channel resilience while maintaining high performance, energy efficiency, and low over- head compared to previous works. Sentry-NoC provides a secure platform for communication among malicious IPs and offers extensive protection against confidentiality and integrity attacks, complete protection against availability attacks. Moreover, it provides protection against side-channel attacks by applying temporal and data obfuscation techniques.

Ultra-Low Power CGRA Architectures for General Purpose Acceleration at the Edge

This project aims at realizing a ultra low power heterogeneous CGRA architecture which can accelerate general purpose workloads at the edge.

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Neural Network Hardware Accelerator at the Edge

This project target to design hardware accelerators targetting neural network applications at the edge, ranging from biomedical applications to computer-vision based applications.

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Laser Attack Benchmark Suite

Laser Attack Benchmark Suite (LABS) aims to complete the security evaluation loop against laser fault injection by allowing circuit designers to test their designs agaist well-known laser fault injection attacks and automatically integrate a hardware-based redundancy technique at the early RTL design stage.

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Using Sensor Inherent Noise in Wearables for User-level Privacy Preservation in Community Data Sensing

This project works on using noise in the sensor to for ensuring privacy-preserving data transfer in embedded systems instead of using additional compute resources for the same

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Enabling smartwatches to sense dehydration and skin health from sweat

This project targeted on leveraging existing sensors available on smartwatches to support dehydration sensing and skin health sensing by integrating a real-time, low-power, highly reusable pH sensor.

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AI-on-skin - On-body AI inference for artificial skin interfaces

This project deals with developing a fast, low-power on-skin AI compute engine (neural network accelerator) to be integrated with current artificial skins.

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