Designing systems with many recording channels is a major challenge in brain-machine interfaces. Power, bandwidth, and size requirements impose tight design constraints for implementing the required processing within an acceptable latency and battery life. Moreover, the variety of brain decoding algorithms require highly versatile systems that can be rapidly adapted to execute different tasks from experiment to experiment as for example microcontrollers (MCUs). However, state of the art MCUs lack of performance and consume too much power to be used as generic platforms for neural decoding applications. To overcome the aforementioned limitations, this paper presents an MCU-based system consisting of a 64-channel event-based neural interface and a Parallel Ultra-Low-Power (PULP) platform that acquires and processes the neural activity. The flexibility of the system (called Neuro-PULP) has been demonstrated through the deployment of two applications: one compressing raw data in streaming mode for wireless transmission, and one generating the cluster and time-stamp of detected spikes, leveraging a low-power event-mode. The event-based approach, coupled with the energy efficiency of the PULP architecture, leads to a more than 4x improvement in energy efficiency with respect to state of the art systems based on FPGAs, leading to the average power consumption of 114μ W /channel, yet retaining the flexibility of fully programmable processor-based architectures.
Neuro-PULP: A Paradigm Shift Towards Fully Programmable Platforms for Neural Interfaces / Schiavone, P. D.; Rossi, D.; Liu, Y.; Benatti, S.; Luan, S.; Williams, I.; Benini, L.; Constandinou, T.. - (2020), pp. 50-54. (Intervento presentato al convegno 2020 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2020 tenutosi a ita nel 2020) [10.1109/AICAS48895.2020.9073920].
Neuro-PULP: A Paradigm Shift Towards Fully Programmable Platforms for Neural Interfaces
Benatti S.;
2020
Abstract
Designing systems with many recording channels is a major challenge in brain-machine interfaces. Power, bandwidth, and size requirements impose tight design constraints for implementing the required processing within an acceptable latency and battery life. Moreover, the variety of brain decoding algorithms require highly versatile systems that can be rapidly adapted to execute different tasks from experiment to experiment as for example microcontrollers (MCUs). However, state of the art MCUs lack of performance and consume too much power to be used as generic platforms for neural decoding applications. To overcome the aforementioned limitations, this paper presents an MCU-based system consisting of a 64-channel event-based neural interface and a Parallel Ultra-Low-Power (PULP) platform that acquires and processes the neural activity. The flexibility of the system (called Neuro-PULP) has been demonstrated through the deployment of two applications: one compressing raw data in streaming mode for wireless transmission, and one generating the cluster and time-stamp of detected spikes, leveraging a low-power event-mode. The event-based approach, coupled with the energy efficiency of the PULP architecture, leads to a more than 4x improvement in energy efficiency with respect to state of the art systems based on FPGAs, leading to the average power consumption of 114μ W /channel, yet retaining the flexibility of fully programmable processor-based architectures.
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