1. Demonstration of STDP based Neural Networks on an FPGA
Kuldeep Singh

2. Overview Aim and Motivation Problem Statement Background Related work
Plan of action

3. AIM
To develop a prototype for demonstrating the feasibility of STDP based neural networks before chip level implementation.
To provide configurability to quickly test the effectiveness of STDP

4. Motivation Actual chip level design a lengthy and expensive process.
Simulations in software are very slow.
Different applications require redesigning of chip

5. Problem
Implementation of neurons, synapses and inter- connects on the FPGA.
Provision for modification of parameters
Need to alter initial weights of the synapses
Specification of configuration
Network topology
Minimization of FPGA resources usage

6. STDP based Learning
Synaptic weights are modified according to time of the spike at the pre-synaptic input.
Synapses helping to spike are strengthened(LTP)
Noncontributing synapses are weakened(LTD)

7. STDP (continued…) STDP algorithm
P(t) = exp(−t / τp ) and Q(t) = exp(−t / τq )

8. Related Work
Design of analog/digital simulator dedicated to real-time neurocomputing
(B. Belhadj et al.)

9. Function of Digital hardware
Get neurons config. parameters from the user send them to the ASICs.
Map the neural network topology
Give to user the neural state information
Receive spike events, send the synaptic signals to post-synaptic neurons
Compute STDP and update synaptic weights

10. Distribution of computational tasks in the system

11. Final Implementation 25 neurons in network
126 slices used for implementing STDP
Exponential block is shared in a TDMA manner

12. Analysis Pros Cons Neurons implemented in ASIC Lower power consumption
High density is achieved
Better replication of neural dynamics
Lower power consumption
Cons
Inflexibility
Longer design time
Susceptibility to noise
Thermal noise
Power supply variation
Device variation

13. Plan
Implementation of the neurons, synapses and inter-connects on the FPGA.
Provision for the user to modify the parameters.
GUI for the user to modify the initial parameters and topology
Testing of the final implementation for various testcases.

14. Implementation of STDP
Pre-calculate the synaptic weight changes.
Implement the full algorithm

15. Implementation of a Neuron
One possibility is to use Snider’s neuron model
Start with the basics and mimic the behavior of neuron.

16. Thank You
Questions? Comments? Opinions?