1 / 21

Dense-Near/Sparse-Far Hybrid Reconfigurable Neural Network Chip

Dense-Near/Sparse-Far Hybrid Reconfigurable Neural Network Chip. Robin Emery Alex Yakovlev Graeme Chester. Overview. Motivation System Elements & Structure Current Work Future Work. Previous Work. Artificial neural network Xilinx Virtex-II FPGA Variable precision

richard-dejesus
Download Presentation

Dense-Near/Sparse-Far Hybrid Reconfigurable Neural Network Chip

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Dense-Near/Sparse-FarHybrid ReconfigurableNeural Network Chip Robin Emery Alex Yakovlev Graeme Chester

  2. Overview • Motivation • System Elements & Structure • Current Work • Future Work

  3. Previous Work • Artificial neural network • Xilinx Virtex-II FPGA • Variable precision • Generated using mark-up • Controlled via PC

  4. Previous Work • Exhausted area before routing resource • Synchronous, Low neuron count • No autonomous learning • FPGA routingresources occupy70-90% • Real-time learningawkward

  5. A Neuron

  6. A Network of Neurons • Billions of neurons in the brain • 100 to 3000 connections per neuron • Majority of connections are proximal • Spikes are generally the same

  7. Clusters • Axons of neocortical neurons form connections in clusters

  8. Learning • In the synapse • Plastic connection • Use learning rule • Autonomous insynapse • Wider mechanism mayexist

  9. Motivation • A FPGA-like neural network device would be of interest to neuroscience • Connectivity is also of interest • Observations support a hybrid of local and distal connectivity • More useful with real-time learning

  10. System Elements • Neuron • Synapse • AER Router • AER/Spike Bridge • Routing Resource • Protocol

  11. AER • Address Event Representation • Asynchronous digital multiplexing • Stereotyped digital amplitude events • Nodes share frame of reference • Information is encoded in the time and number of events

  12. Dense-Near Connectivity

  13. Sparse-Far Connectivity

  14. Network Structure

  15. Current Work • Neuron • Configurable threshold • Asynchronous • 7-bit count • Decay • Spike generator

  16. Current Work • Neuron & Spike Generator • 130nm UMC CMOS

  17. Current Work • Software model & protocol refinement • Ongoing work: • Autonomous Synapses • AER Router/Bridges

  18. Evaluation • Topographic map • Compare to popular software modelling tool such as NEURON

  19. Future Work • Long-term learning process • Improve capacity of AER link by grouping spikes • Aggregation of pulse-widths could improve range of dendritic input • Multiplexing of some direct links

  20. Conclusions • Reconfigurable, adaptive neural network system • Real qualities of interest to neuroscientists • Neuron and spike generator manufactured • Interesting avenues for further work

  21. Thank you r.a.emery@ncl.ac.uk

More Related