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Conventional processors, like CPUs and GPUs, are efficient in processing dense, synchronously delivered data structures, not sparse, asynchronous event streams. Throughput is maintained by keeping the instruction and data pipelines as full as possible, even at the cost of executing redundant computations on unchanging data [Ref].
Recently, a new generation of natively event-based neuromorphic processors has appeared that can operate on sensor event streams directly [Ref.]. These many-core systems instantiate large populations of spiking neurons in massively parallel, low-power hardware, and inherit all of the advantages of event-based sensors, such as efficient, data-driven resource consumption[ Ref.].
Processing latency can be as fast as the event propagation time through the longest chain of neurons, so a neural network running on these systems can react to a stimulus in tens of milliseconds, fast enough to identify quick motions like hand gestures in real-time.
Brain-Computer Interfaces (BCI) are emerging devices that enable users to interact with computers by means of brain-activity only, this activity is generally measured by EEG sensors. BCI has various application, such as in the field of multimedia, virtual reality or video games. BCI also has other applications, such as for paralysed people, where it can be the only means of communication with the external world.
From another hand, the neuromorphic computing paradigm promises to drastically improve critical computational tasks' efficiency, such as decision-making and perception. Unlike the typical artificial neural networks (ANNs) where neurons fire at each propagation cycle, the neurons in a neuromorphic neural networks model, named spiking neural network (SNNs), fire only when a membrane potential reaches a specific value. Spiking neurons are only activated when sufficient signals are integrated from other neurons, which leads to sparse neural activities at the network level.
The goal of this project is to first understand the role of spike-based learning in neuromorphic systems considering various constraints that are not usually considered in simulations, such as the effect of variability in the neural network parameters or the impact of bounded weights in the learning/training phase. The final goal is to study EMG signal processing and classification based on DNN algorithms towards the development of adaptive anthropomorphic robot arm/hand.
|Date|Task|
| Mid of Sept ~ October 8 2020 | Prepare slides for the monthly meeting |
| December 1 | Draft submission deadline for ETLTC2021 |
