milliSoC is a reusable System-on-Chip (SoC) reference platform being developed by SoC Labs to simplify the design and implementation of application-specific processors for real-time embedded systems and edge artificial intelligence (AI). Edge based systems are often characterised by the need for real-time control, interfaces to the real world and the addition of AI. The term Physical AI has been used to describe such systems that can sense, decide, and act in real time. 

The goal of this project is to design and train a CNN model for spoken digit classification and deploy it on a low-cost system-on-chip (SoC) built around an ARM Cortex-M0 microcontroller with a dedicated CNN accelerator. The SoC will be deployed as a prototype on an FPGA. Its throughput, latency and energy consumption per inference, along with its FPGA resource utilisation, will then be evaluated and compared against other systems.

A custom chip project for recording and stimulating nerve/muscle signals, sensing an electrode's electrical activity and delivering safe, controlled electrical pulses back to it. Built around an analog front-end and dual-mode data converters for capturing both fast signal spikes and slow baseline drift, plus a closed-loop stimulation path with hardware-level safety limiting to keep delivered current within safe bounds.

This project focuses on the design and FPGA implementation of a low-power hardware accelerator for a Finite Impulse Response (FIR) filter, intended to reduce noise in speech signals before speech recognition. The accelerator uses a 32-tap, 16-bit serial multiply-accumulate architecture that reuses one signed multiplier and one accumulator to reduce hardware cost and energy consumption. The FIR algorithm is verified using a MATLAB model and RTL simulation.

In this project, we would like to integrate our TsetlinMachine Hardware Accelerator into nanoSOC and tapeout the design.

To enable full operating system support in megaSoC, a substantial amount of memory is required to accommodate the complexity of modern Linux-based software stacks, including the kernel, drivers, middleware, and user-space applications. Linux environments, unlike lightweight bare-metal systems such as nanoSoC, demand not only larger memory footprints but also consistent access to high-bandwidth memory to maintain performance across multitasking workloads. The most common approach is to incorporate external DRAM, which offers the necessary capacity and throughput.

Precision timekeeping is a foundational service in any distributed system. Whether synchronising Ethernet frames to a PTP grandmaster, timestamping die-to-die packet exchanges between chiplets, or scheduling time-critical hardware events, the system needs a clock that is accurate, capturable at multiple points simultaneously, and adjustable by both hardware servo loops and software without stopping.