This is all about the practical details of getting a startup off the ground. It's a personal view based on experience (my UK list), many things are easy when you've done them a few times but can catch you out first time.

Practice having good ideas:

• Having one good idea is rarely enough, things will change, you'll need to keep having good ideas
• Work out how you think best (for me it's long distance running with an A7 note pad and pencil)
• Read Thinking, Fast and Slow e.g. to understand your inherent bias towards your idea
• Keep a scrapbook/wiki of all ideas, it's good to go back and see what didn't go anywhere

Pick the best idea:

• What is the company going to do?
• Who are the competitors?
• Why is it unique?
• How is it going to be successful?
• Draft a quick business plan so that you have a story to tell others
• Buy a dedicated bound notebook and write all thoughts and meetings down
• Remember: If your idea seems to good to be true then it probably is

Good, that's the easy bit done!

Now do what is cheap and easy without spending much money (less than £1,000). The aim of this stage is to flesh out the idea without you becoming emotionally attached to it, you should be able to decide that it's not going to work and walk away:

• Read The Lean Startup
• Listen to Sam Altman - How to Succeed with a Startup
• Establish relations to stress test your idea:
  • Go to networking groups - don't be afraid of talking
  • Can you explain it to friends and family?
  • Make sure you get honest not sympathetic answers
• Pull together cofounders, especially if this is your first time
  • Research and discuss the equity split, e.g. The Very First Mistake Most Startup Founders Make
• Find friendly potential customers and get to know them and what their pains are
• Get your IP story straight (think what is patentable and what might infringe on others)
• Find a good name - hopefully you will be stuck with it for a long time
  • Register domains, twitter, facebook, reddit, etc
  • Register with companies house
  • Register with HMRC and file null returns every year
• Get a free business bank account but don't trade (that would get you into CT600's)
• Pick a good enough logo, think of a tag line
  • Get a few business cards
  • Put up a basic web site
  • Print a few T-shirts - they'll get people talking
• Make a record of all expenses in a simple spreadsheet and keep all receipts
• Pace yourself - this is a marathon not a sprint, it will take longer than you think and overcommitting can destroy everything

Now find some quiet time to ask yourself: Is it really a good idea?

If you are really sure you can take the pain, then go for it:

• Register for VAT as you'll not make a profit so best to pay no VAT
• Register for PAYE
• Find a good accountant (I can recommend Savvy)
  • Switch to their expenses tracking spreadsheet/package
  • Claim back initial expenses
• Join a local networking group to talk about your blockers
• Decide on your risk profile:
  • Is it better to spend fast and hope to be first or take less risk?
  • How much time you want to spend on raising money?
  • How much you care about giving your investors a decent return?
• Learn the basics of seed funding (not forgetting SEIS/EIS):
  • Look at Seedrs, SyndicateRoom, Envestors, Crowdcube and maybe progress to Green Angel Syndicate:
    • Get to know the site
    • Get to know what makes a strong pitch
    • Get to know common themes (apps, chips, bikes, etc)
    • Invest small amounts (over years if you have the time)
  • Look at grants (e.g. SMART grants from Innovate UK)
• Write a short, easily digestible business plan (if your idea is good it will be rapidly out of date)
• Start on IP protection (patents, trademarks)

Congratulations, you now have a Startup!

Things to remember/read:

• It will be a rollercoaster, scary and exhilarating. “If everything seems under control, you're not going fast enough.”
• Managing people is a huge overhead. You may think you are starting a tech company, you're not, it'll all be people management. The larger you are the harder it is to innovate, so get the foundations right before you scale the people.
• Reid Hoffman’s 10 rules of entrepreneurship
• Don't do things properly
• Ideas are just a multiplier of execution
• The Lean Startup again!
• Keep everything simple By the time you setup Airflow + Docker + Jenkins to your liking, the dude using cron + shell scripts is on his third failed startup.
• Do the right thing, act responsibly and with integrity. Your reputation is more important than your current startup.
• Evolution and Revolution as Organizations Grow

Neuracore was incorporated on 12 December 2018 and dissolved on 30 April 2019.

Design and license cores for the efficient execution of neural networks. This will enable “AI Everywhere”.

Extreme power efficiency obtained though low precision integer operation (with supporting software): single propagation delay addition and very low propagation delay multiplication.

Licence technology into a massive market, from servers though laptops, phones and smart watches.

Hardware acceleration for Neural Nets is already huge, the whole current wave of Deep Learning happened because GPUs became cheap enough. Google have enough services that need NNs to build their own ASIC, the TPU. Facebook is driven by AI, the trend towards increasing automation is massive and well known.

• Stage 0: Come up with a reasonable hardware design
• Stage 1: Do patent review then get team together
• Stage 2: Partner with ARM (local and known), get them to fund joint work
• Stage 3: Sell to ARM, boarden base. Retain sufficient IP to be independent.

Aim for getting it out there in 5 years - any sooner and FPGA will dominate, any later and too much risk.

Use AI index 2018 annual report for evidence of AI gold rush. Neuracore sells the shovels “During the gold rush its a good time to be in the pick and shovel business” Mark Twain

From: The Great Debate of AI Architecture

• Nvidia - DNN training is a major part of their strategy
• Intel (Nervana (estimated $408 million) and Movidius) - Need to maintain leading position
• ARM ML Processor - FPGA to rewire a fixed point unit with local controller and memory. Claim 4 TOps/s per Watt.
• Google - have volume, have built TPU and TPU2
• Microsoft BrainWave - catch up with Google
• Baidu SDA - no definitive reference - https://www.nextplatform.com/2017/08/22/first-look-baidus-custom-ai-analytics-processor/
• Xilinx - DeePhi Tech
• IBM and fp8 https://www.ibm.com/blogs/research/2018/12/8-bit-breakthroughs-ai/
• ESE - not sure what this refers to - maybe https://arxiv.org/abs/1612.00694
• Teradeep IP licence of RTL for SoC and FPGA crunchbase
• Cerebras raised $112m crunchbase
• Graphcore raised $310m cruchbase Claim 0.5 ExaFlop per rack (assume 2-20kW), so 250-25 TFlop per Watt.
• Groq raised $62m crunchbase claim 8 TOps/s per Watt
• Wave Computing raised $203m crunchbase
• Global Deep Learning Chipsets Market 2019-2025: Markets Major Players Are-, Google, Intel, Xilinx, AMD...

• Consumer/research grade has to be:
  • Faster than GPU, FPGA or TPU
  • Cheaper than GPU and FPGA (e.g. has more RAM)
  • Easy enough to use (will be less precision than fp16)
• Need to get memory side-by-side with logic so get the bandwidth
• Must be able to do training on chip as something will need this in 5 years time, e.g. AGI
• Must be flexible enough to keep up with the NN developments in the next 5 years, including training
• Hardware people have fixated on CNNs - are they right? What does everyone want to use?
• Must be able to use all common SGD optimisation techniques.

If we assume that neural nets will be a major consumption of power in the future, and that power is limited by convenience (on a phone) or cost (servers) or CO2 emissions (climate change) then there is the case for a power efficient hardware implementation of neural networks.

DNNs are everywhere and are growing in popularity, however the popular hardware is very general and not power efficient. This limits both the scale which can be trained and the scope for deployment. Typically a 2 slot PCIe card can consume 300W and a small number of them can fit in a server. GPUs from Nvidia are the current favourite, these perform fp16 calculations (was fp32) using a dedicated architecture of local SIMD processors and local data. FPGAs are also receiving more attention, they are good at convolutional neural networks (say why). Any 10x improvement over current technology must both reduce the transistor count (so as to reduce power) and be very memory bandwidth efficient (so as not to have a memory bottleneck). The field is moving fast, so it must be easily adoptable in a short time period.

In order to make an impact any solution must be complete, that is almost invisible to the user. It needs to improve on the three major operations:

• forward: The inference, or forward pass of a DNN model
• backward: The backward error propagation pass of stochastic gradient descent which accumulates gradients over a batch
• update: The work needed to scale the batch gradient into a weight update (may need complex CPU like operations)

After considering many designs, including analog and ternary weights, I ended up with 4 bit weights and activations. This achieves the goals albeit uncomfortably similar to the TPU. The scale of work needed to make the trainsition from fp32/fp16 to 4bit is too great - the first prototype would be noticed by the giants and the company would be overtaken (defending IP is very expensive). This could well lead to a forced sale which isn't great for anyone (expecially founders/Ordinary share holders).

Start October 2018, end February 2019, minimal external costs.

EDIT: Reopened 4 Nov 2021