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.

This program is dedicated to the development of a System on Chip (SoC) platform, specifically designed to support learning and research activities within Indonesian academic institutions. The platform serves as an educational and research tool for students, lecturers, and researchers to gain hands-on experience in digital chip design.

The instruction memory in the first tape out of nanosoc was implemented using SRAM. The benefit was the read bandwidth from this memory was very fast, the downside was on a power-on-reset, all the code was erased as SRAM is volatile memory. An alternative use of non-volatile memory would benefit applications where  deployment of the ASIC does not allow, or simply time is not available for programming the SRAM after every power up. 

The Arm Cortex-M55 AIoT SoC design platform is an AIoT subsystem that allows custom SoC designers to integrate their hardware circuits and embedded software for differentiation. The platform is developed by TSRI (Taiwan Semiconductor Research Institute) to support academic research on SoC design. It's built on the Arm Corstone-300 reference package, featuring the Cortex-M55 CPU and Ethos-U55 NPU.

As part of plans for continued development of nanoSoC one area that requires improvement is the power structure of system. The first iteration of nanoSoC contained 2 power domains: the accelerator domain and the remainder of the SoC. Both power domains were connected to external pins to allow connection to separate external voltage regulators and power measurement ICs, as implemented in the first version of the nanoSoC testboard.

There is growing interest within the SoC Labs community for an Arm A-Class SoC that can support a full operating system, undertake more complex compute tasks and enable more complicated software directed research. The Cortex-A53 is Arm's most widely deployed 64-bit Armv8-A processor and can provide these capabilities with power efficiency.