Weight quantization

[neworderofjamie]

I am going to add support to the e-prop and inference compilers before I merge this but thought I'd open a PR for a sanity check. Basic design is:

Quantisation helpers

Take some percentile (99% of un-quantised weight distribution) and fit a fixed point format to it

Quantisation callbacks

Pulls weights, calculates quantised version using helper and pushes

Event prop compiler

• Uses standard g variable for quantised version of the weight so, when checkpoints are loaded into inference compiler, quantised weights these will be used
• Adds second gBack to trainable weights to hold unquantised weight which gradients get applied to
• At beginning of each batch, quantisation callback is applied to
• Untrained weights are quantised in place, either with a callback at the start of training if they are initialised using GeNN or by calling the helper directly if raw values are provided.

This does assume each synapse group (in GeNN speak) has its own scaling factor - will need to confirm whether this is the case on Loihi.
Training with Eventprop, 8-bit weights don't cause any problems with MNIST SHD example goes from XXX to 63.9% accuracy.

Quantised weight distribution (note quantisation artefacts in long tails):

[codecov-commenter]

Codecov Report

Attention: Patch coverage is 37.81513% with 74 lines in your changes are missing coverage. Please review.

Project coverage is 61.48%. Comparing base (8822f90) to head (ab24e11).

Files	Patch %	Lines
ml_genn/ml_genn/compilers/event_prop_compiler.py	10.34%	26 Missing ⚠️
ml_genn/ml_genn/utils/quantisation.py	21.21%	26 Missing ⚠️
ml_genn/ml_genn/compilers/weight_quantisation.py	50.00%	13 Missing ⚠️
ml_genn/ml_genn/compilers/inference_compiler.py	70.96%	9 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master      #88      +/-   ##
==========================================
- Coverage   62.19%   61.48%   -0.72%     
==========================================
  Files         100      102       +2     
  Lines        3896     4011     +115     
==========================================
+ Hits         2423     2466      +43     
- Misses       1473     1545      +72     

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.