Notes

Neuromorphic Engineers Build Hardware that Seeks to Emulate Neural Networks Instead of Simulating Them To make things more efficient, we would like to create an hardware that does most of the simulation through physical properties rather than simulating them through software. One major issues of these hardware is related to Device Mismatch (a stumbling block for widespread use of analog neuromorphic hardware), i.e., a slight difference in membrane potential among chips due to manufacturing variability, which is not found in software simulations. So, can analog neuromorphic substrates self-calibrate through surrogate gradient learning and overcome device mismatch? To study this question we used BrainScaleS-2 analog neuromorphic hardware system and if you record from a neuron in this chip when a current is injected you can capture the analog voltage through an oscilloscope.

In-the-loop Surrogate Gradient Training Forward-pass on chip and backward pass in software.

1. Forward pass on chip.
2. Measure on-chip analog voltage traces.
3. Inject true voltages into comp. graph.
4. Compute surrogate gradients update weights.

Functional spiking neural networks trained on accelerated analog neuromorphic hardware. We show that on the MNIST example training loss goes basically to zero. Surrogate gradient learning self-calibrates the analog neuromorphic substrate.

• Surrogate gradients with BPTT are a powerful tool to build functional spiking neural networks.
• Can instantiate sparsely active networks.
• Voltage-aware in-the-loop training with surrogate gradients self-calibrates neuromorphic hardware.