Research Interests

Energy-efficient Edge devices

Battery-powered embedded systems are increasingly used in edge applications such as wearables, sensor nodes, and IoT devices. Their small form factor limits battery capacity and computing resources, while growing demands for multimodal sensing, privacy, low latency, and edge AI push more processing to the edge and increase energy consumption. The rapid evolution of edge AI also necessitates reconfigurability, field-update capability, and shorter development cycles. These challenges require platforms that combine computational power, parallelism, flexibility, and field reconfigurability within tight energy budgets. FPGAs offer a middle ground; however, they are not as power-efficient as low-power MCUs and ASICs. Our research focuses on optimization strategies to harness FPGAs energy-efficiently in such applications.