This project aims to develop an advanced RF energy harvesting (EH) receiver chip specifically designed to power embedded sensors for monitoring the condition of groceries during transportation. The receiver chip captures wireless energy transmitted from phased array antennas and converts it into electrical power that is used to operate onboard sensors, which continuously monitor critical parameters such as temperature and humidity inside delivery trucks.

Stroke and epilepsy are among the most common debilitating neurological conditions, with a worldwide prevalence of 100 million people (World Stroke Organization, 2022) and 50 million people (World Health Organization, 2024), respectively. Present-day approaches for treating neurological and neurosurgical conditions include physiotherapy, pharmacological treatment, surgical excision, and interventions such as deep brain stimulation.

Spiking Neural Networks (SNNs) require processing a large number of spikes to achieve high classification accuracy. However, this results in frequent memory accesses to fetch synaptic weights, which significantly increases energy dissipation in SNN systems. To address this challenge, we propose a unique technique called the Repetitive Spike Train (RST) method. By exploiting the temporal similarity of spike trains across time steps, RST minimizes redundant spike train updates and reduces memory read/write operations.

Aspen is a unified accelerator for deep neural network (DNN)-based extended reality perception workloads. Aspen proposes a mixed-precision quantization scheme using the posit datatype to reduce memory usage while maintaining accuracy, a DNN accelerator for mixed-precision posit datatypes, and efficient data prefetching and data layout to minimize data reorganization. The Aspen system-on-chip has an Arm Cortex-M3 CPU, a mixed-precision posit-based DNN accelerator, and 4 megabytes of SRAM partitioned into eight 512 KB banks, connected through a 128-bit-wide interconnect.