Anteneh Gebregiorgis

Muhammad Ali Siddiqi, David Vrijenhoek, Lennart P. L. Landsmeer et al.

Electrophysiological recordings of neural activity in a mouse's brain are very popular among neuroscientists for understanding brain function. One particular area of interest is acquiring recordings from the Purkinje cells in the cerebellum in order to understand brain injuries and the loss of motor functions. However, current setups for such experiments do not allow the mouse to move freely and, thus, do not capture its natural behaviour since they have a wired connection between the animal's head stage and an acquisition device. In this work, we propose a lightweight neuronal-spike detection and classification architecture that leverages on the unique characteristics of the Purkinje cells to discard unneeded information from the sparse neural data in real time. This allows the (condensed) data to be easily stored on a removable storage device on the head stage, alleviating the need for wires. Synthesis results reveal a >95% overall classification accuracy while still resulting in a small-form-factor design, which allows for the free movement of mice during experiments. Moreover, the power-efficient nature of the design and the usage of STT-RAM (Spin Transfer Torque Magnetic Random Access Memory) as the removable storage allows the head stage to easily operate on a tiny battery for up to approximately 4 days.

Zacharia A. Rudge, Dario Izzo, Moritz Fieback et al.

In recent years, the space community has been exploring the possibilities of Artificial Intelligence (AI), specifically Artificial Neural Networks (ANNs), for a variety of on board applications. However, this development is limited by the restricted energy budget of smallsats and cubesats as well as radiation concerns plaguing modern chips. This necessitates research into neural network accelerators capable of meeting these requirements whilst satisfying the compute and performance needs of the application. This paper explores the use of Phase-Change Memory (PCM) and Resistive Random-Access Memory (RRAM) memristors for on-board in-memory computing AI acceleration in space applications. A guidance and control neural network (G\&CNET) accelerated using memristors is simulated in a variety of scenarios and with both device types to evaluate the performance of memristor-based accelerators, considering device non-idealities such as noise and conductance drift. We show that the memristive accelerator is able to learn the expert actions, though challenges remain with the impact of noise on accuracy. We also show that re-training after degradation is able to restore performance to nominal levels. This study provides a foundation for future research into memristor-based AI accelerators for space, highlighting their potential and the need for further investigation.

Alexandra Dobrita, Amirreza Yousefzadeh, Simon Thorpe et al.

For Edge AI applications, deploying online learning and adaptation on resource-constrained embedded devices can deal with fast sensor-generated streams of data in changing environments. However, since maintaining low-latency and power-efficient inference is paramount at the Edge, online learning and adaptation on the device should impose minimal additional overhead for inference. With this goal in mind, we explore energy-efficient learning and adaptation on-device for streaming-data Edge AI applications using Spiking Neural Networks (SNNs), which follow the principles of brain-inspired computing, such as high-parallelism, neuron co-located memory and compute, and event-driven processing. We propose EON-1, a brain-inspired processor for near-sensor extreme edge online feature extraction, that integrates a fast online learning and adaptation algorithm. We report results of only 1% energy overhead for learning, by far the lowest overhead when compared to other SoTA solutions, while attaining comparable inference accuracy. Furthermore, we demonstrate that EON-1 is up for the challenge of low-latency processing of HD and UHD streaming video in real-time, with learning enabled.