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

clangedbivalveΤεχνίτη Νοημοσύνη και Ρομποτική

19 Οκτ 2013 (πριν από 3 χρόνια και 10 μήνες)

90 εμφανίσεις

Dense
-
Near/Sparse
-
Far

Hybrid Reconfigurable

Neural Network Chip

Robin Emery

Alex Yakovlev

Graeme Chester

Overview


Motivation


System Elements & Structure


Current Work


Future Work

2

Previous Work

3


Artificial neural network


Xilinx Virtex
-
II FPGA


Variable precision


Generated using mark
-
up


Controlled via PC

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

A Neuron

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

Clusters

7


Axons of neocortical neurons form
connections in clusters

Learning


In the synapse


Plastic connection


Use learning rule


Autonomous in

synapse


Wider mechanism may

exist

8

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

System Elements


Neuron


Synapse


AER Router


AER/Spike Bridge


Routing Resource


Protocol

10

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

Dense
-
Near Connectivity

12

Sparse
-
Far Connectivity

13

Network Structure

14

Current Work

15


Neuron


Configurable threshold


Asynchronous


7
-
bit count


Decay


Spike generator

Current Work

16


Neuron & Spike Generator


130nm UMC CMOS

Area

1145.6
μ
m
2

(90nm: 700
μ
m
2
)

Gates

390

Density

873 p. mm
2

(90nm:

1429 p. mm
2
)

Spike Period

4.5ns

Generated Clock
Frequency

160MHz

Max. Spike Rate
(
theshold
=100)

2.35 million p. second

Current Work


Software model & protocol refinement


Ongoing work:


Autonomous Synapses


AER Router/Bridges

17

Evaluation


Topographic map


Compare to popular software modelling
tool such as NEURON

18

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

Conclusions


Reconfigurable, adaptive neural network
system


Real qualities of interest to
neuroscientists


Neuron and spike generator
manufactured


Interesting avenues for further work

20

Thank you

r.a.emery@ncl.ac.uk