[WIP] perf: common neural network workloads benchmarks

[avik-pal]

currently add CPU versions. should be easy to extend to GPU once SciMLBenchmarks has GPU runners available.

TODOs

• [ ] Restore Manifest to use the released versions of packages
• [ ] Boilerplate
  • [x] Inference
  • [x] Inference Plots
  • [ ] Training
  • [ ] Training Plots
• [ ] Models
  • [ ] Simple MLP
    • [x] Inference
    • [ ] Training
  • [ ] Simple MLP with BN -- wait on https://github.com/LuxDL/LuxLib.jl/pull/126
    • [ ] Inference
    • [ ] Training
  • [ ] LeNet
    • [x] Inference
    • [ ] Training
  • [ ] ResNet
    • [ ] Inference
    • [ ] Training
• [ ] Add Reactant Inference Code
  • Training code will have to wait till the compile PR lands in Lux

[ChrisRackauckas]

@thazhemadam @staticfloat what's the easiest way to set this up so that benchmarks can choose a separate runner for GPUs?

[avik-pal]

Allowing different runners based on the jmd metadata would be a nice way. Something like

---
title: Simple Neural Networks
author: Avik Pal
backend: CUDA <--- If nothing present then use CPU. In future we can allow AMDGPU/Metal/etc with the same syntax
---

[avik-pal]

Lux now matches SimpleChains in inference timings :sweat_smile:. The ones where we fall behind are because Octavian is somewhat slow on EPYC machines so it is turned off