EventSession Formset ID 137

Event: CCNW 2022
Type: Work Group
Title: SpiNNaker, event-based sensors, and BitBrain
Description: This workshop is intended for people interested in working with SpiNNaker. Tutorials for using SpiNNaker: Our particular aim is to implement BitBrain on SpiNNaker neuromorphic platform and learn event-based stimuli from sensors. This work was presented during NICE conference 2022: BitBrain is a learning algorithm based upon a novel synthesis of ideas from sparse coding, computational neuroscience and information theory that support single-pass learning, accurate and robust inference, and the potential for continuous adaptive learning. They are designed to be implemented efficiently on current and future neuromorphic devices as well as on more conventional CPU and memory architectures. The SBC memory stores coincidences between features detected in class examples in a training set, and infers the class of a previously unseen test example by identifying the class with which it shares the highest number of feature coincidences. A number of SBC memories may be combined in a BitBrain to increase the diversity of the contributing feature coincidences. The resulting inference mechanism is shown to have excellent classification performance on benchmarks such as MNIST and EMNIST, achieving classification accuracy with single-pass learning approaching that of state-of-the-art deep networks with much larger tuneable parameter spaces and much higher training costs. It can also be made very robust to noise. BitBrain has some similarities to kernel-based classification methods, and this relationship is discussed in detail. However, BitBrain has much lower computational requirements than kernel methods (and much lower than deep networks) both in training and in inference, and is therefore well-suited to edge applications where it could also interface efficiently with event-based sensors such as silicon retinas.
Moderators: <QuerySet [<User: mich_hopk>]>
Schedule ID start time end time location
165 May 3, 2022, 4 p.m. May 3, 2022, 5 p.m. Lecture room
203 May 5, 2022, 9 p.m. May 5, 2022, 10 p.m. Lecture room (-> disco?)
230 May 10, 2022, 3 p.m. May 10, 2022, 4 p.m. Lecture room (-> disco?)
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