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# System prepended metadata

title: Outline

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# Outline

### Introduction

### Background
    1. xxx
    2. xxx
    
### Methodology
    1. Sparsity
        * What sparsities exist in the model?
        * Why do we choose this sparsity?
            * Why vecDotProduct?
            * What approximation/assumption we make?
            
    2. Optimiser
        * How Optimiser works
        * How sparse vecDotProduct constraints are encoded?
        * How to work out the number of instances
        
    3. Hardware
        * Original design
        * Sparse design
        * Choice of different M/N combinations

### Experiments
    1. Metric
    3. M-N scales well?
    4. Overall results
    5. Case study if we have space
    
    
## Tasks

- [ ] introduce timing to optimiser
- [ ] decision between sparse and dense versions for each layer
- [ ] M-to-N arbiter heuristic

