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.


Older posts…


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.

Read more »

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

Read more »

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.

Read more »

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.

Read more »

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.

Read more »

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.

Read more »