Tinygrad: A simple and powerful neural network framework
There's an interesting roadmap in the "cherry" folder of the git repo[0].  It begins by bringing up a design on FPGA and ends with selling the company for $1B+ by building accelerator cards to compete with NVIDIA:<p><pre><code>  Cherry Three (5nm tapeout)
  =====
  * Support DMA over PCI-E 4.0. 32 GB/s
  * 16 cores
  * 8M elements in on board RAM of each core (288 MB SRAM on chip)
  * Shared ~16GB GDDR6 between cores. Something like 512 GB/s
  * 16x 32x32x32 matmul = 32768 mults
  * 1 PFLOP @ 1 ghz (finally, a petaflop chip)
  * Target 300W, power savings from process shrink
  * This card should be on par with a DGX A100 and sell for $2000

  * At this point, we have won.
  * The core Verilog is open source, all the ASIC speed tricks are not.
  * Cherry will dominate the market for years to come, and will be in every cloud.
  * Sell the company for $1B+ to anyone but NVIDIA
</code></pre>
[0] <a href="https://github.com/geohot/tinygrad/blob/master/accel/cherry/README#L22-L68" rel="nofollow">https://github.com/geohot/tinygrad/blob/master/accel/cherry/...</a>