1. Dense-Near/Sparse-Far Hybrid Reconfigurable Neural Network Chip Robin Emery Alex Yakovlev Graeme Chester

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

3. Previous Work 3 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 routing resources occupy 70-90% Real-time learning awkward 4

5. 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 6

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

8. Learning In the synapse Plastic connection Use learning rule Autonomous in synapse Wider mechanism may exist 8

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 9

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

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 11

12. Dense-Near Connectivity 12

13. Sparse-Far Connectivity 13

14. Network Structure 14

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

16. Current Work 16 Neuron & Spike Generator 130nm UMC CMOS Area1145.6μm 2 (90nm: 700μm 2 ) Gates390 Density873 p. mm 2 (90nm: 1429 p. mm 2 ) Spike Period4.5ns Generated Clock Frequency 160MHz Max. Spike Rate (theshold=100) 2.35 million p. second

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

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

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 19

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

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