CenBRAIN Neurotech's New Publications in IEEE AICAS'23

Titled “NeuroBMI: A New Neuromorphic Implantable Wireless Brain Machine Interface with A 0.48 uW Event-Driven Noise-Tolerant Spike Detector” and “PN-TMS: Pruned Node-fusion Tree-based Multicast Scheme for Efficient Neuromorphic Systems”, these contributions have been accepted in the 2023 IEEE International Conference on Artificial Intelligence Circuits and Systems conference. Congratulations to authors and co-authors for these achievements.

Paper #1

Chen, J., Wu, H., Liu, X., Eskandari, R., Tian, F., Zou, W., ... & Sawan, M. (2023, June). NeuroBMI: A New Neuromorphic Implantable Wireless Brain Machine Interface with A 0.48 µW Event-Driven Noise-Tolerant Spike Detector. In 2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS) (pp. 1-5). IEEE.

Fig. 1： Conventional BMIs and the proposed neuromorphic implantable wireless BMI. (a) Nyquist sampling + raw data transmission; (b) Nyquist sampling + on-implant spike detection; (c) Proposed neuromorphic implantable wireless BMI: Unified neuromorphic sampling, processing and transmission.

Abstract

The use of Brain-Machine Interfaces (BMIs) in neuroscience research and neural prosthetics has seen widespread application. With the development of implantable wireless BMIs featuring increasing channel counts, the volume of data generated requires impractically high bandwidth and transmission power for the implants. In this paper, we present NeuroBMI, a novel neuromorphic implantable wireless BMI that leverages a unified neuromorphic strategy for neural signal sampling, processing, and transmission. The proposed NeuroBMI and neuromorphic strategy reduces transmitted data rate and overall power consumption. NeuroBMI takes into account the high sparsity of neural signals by employing a integrate-and-fire sampling based analog-to-spike (ATS) converter, which generates digital spike trains based on level-crossing events and avoids unnecessary data sampling. Additionally, an event-driven noise-tolerant spike detector and event-driven spike transmitter are also proposed, to further reduce the energy consumption and transmitted data rate. Simulation results demonstrate that the proposed NeuroBMI achieves a data compression ratio of 520, with the proposed spike detector consuming only 0.48 uW.

Paper #2

Shen, Z., Fang, C., Tian, F., Yang, J., & Sawan, M. (2023, June). PN-TMS: Pruned Node-fusion Tree-based Multicast Scheme for Efficient Neuromorphic Systems. In 2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS) (pp. 1-5). IEEE.

Fig. 1: The routing map of the proposed PN-TMS algorithm.

Abstract

A growing demand for low-power and real-time computation is motivating the development of dedicated neuromorphic processors. To maximize scalability and power efficiency, multicore architecture has been broadly applied in existing neuromorphic processors. Nevertheless, mapping a Spiking Neural Network (SNN) on a multicore architecture requires a lot of multicast operations. Conventional routing algorithms like path-based routing and dimension order routing (DOR) lead to a severe overhead in both latency and power. To address these limitations, we propose a novel routing algorithm named Pruned Node-fusion Tree-based Multicast Scheme (PN-TMS). PN-TMS leverages multiple algorithms for route planning, optimizing latency and power simultaneously. Experiment results show that PN-TMS outperforms existing network processors’ routing schemes in terms of both energy consumption and latency, achieves an average energy delay product (EDP) reduction of 38.9%.