An Implantable Neuromorphic Sensing System Featuring Near-Sensor Computation and Send-on-Delta Transmission for Wireless Neural Sensing of Peripheral Nerves

Abstract

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Architectural Exploration of Neuromorphic Compression based Neural Sensing for Next-Gen Wireless implantable-BMI

“…This approach essentially 'morphs' the initial sensory information processing stages of biological sensors/receptors into VLSI chips using a combination of efficient analog and digital circuitry to mimic their asynchronous spatio-temporal activity. A neuromorphic event-driven neural recording approach was first proposed in [34] for iBMI and more recently implemented in [35]- [37]. The neuromorphic approach can also provide benefits of digital multiplexing due to integration with address event representation (AER) circuits and digitizing/communicating data only during spikes by virtue of in-pixel thresholding.…”

“…The AER-based handling of events could potentially address the shortcomings of data loss due to collisions in SPDWOR configuration. Works in [20], [35], [37] introduce a pipeline involving spike train generation using an analog-to-spike conversion block on-chip decoding using a spiking neural network, which limits its usability to specific tasks. While [35] uses a round-robin arbitration based AER scheme, [20], [37] does not have a collision handling strategy that could scale with the size of the neural electrode.…”

“…Works in [20], [35], [37] introduce a pipeline involving spike train generation using an analog-to-spike conversion block on-chip decoding using a spiking neural network, which limits its usability to specific tasks. While [35] uses a round-robin arbitration based AER scheme, [20], [37] does not have a collision handling strategy that could scale with the size of the neural electrode. None of the aforementioned neuromorphic/eventbased neural recording schemes investigate the gains from using the neuromorphic compression in terms of reduction in data rate, nor assess its impact on iBMI performance.…”

“…We do not present circuit simulation of the Asynchronous Delta Modulator since such schemes have been published in several papers such as [34] or even the recent [35], [36]. Unlike SPDWOR, no expensive wiring is needed to handle collision scenarios, which are inherently handled by the AER-based readout strategy in our proposed pipeline.…”

“…Neuromorphic circuits are known for low power consumption, and thus a neuromorphic compression based iBMI system is expected to consume far less power than conventional neural recording systems with full-sample transmission. Relying on the hardware measurements from [35] which implements an ADM-based pulse transmission in 40 nm CMOS technology, the ADM configured consumes ≈ 7 µW per channel. A neuromorphic compression based readout as proposed in this work, operating in low-power mode can be estimated to result in a surface power density of ≈ 4.36 mW/cm 2 , which lies well within the power dissipation budget of ≈ 80 mW/cm 2 for iBMI [14].…”