3 ms·
Interesting idea. The main question for me is if it will be efficient, in the sense that you need program/models that can be binpacked into the size of your de
by rlupi 2y ago
Interesting idea.
The main question for me is if it will be efficient, in the sense that you need program/models that can be binpacked into the size of your design and need data all at the same time in various stages otherwise a lot of your silicon will be under-utilized (since you don't have memory, you can't trade between compute and RAM to efficiently use your silicon die size).
Rather than our current breed of neural network architectures and models, you'd probably need to look into alternatives like spiking neural network and see if they can store data as frequency and activation patterns.
- RetroTechie 2y ago> since you don't have memory, you can't trade between compute and RAM to efficiently use your silicon die size As I understand it, BitGrid would be a nice architecture for applications which: # Are compute-heavy # Don't need a lot, or high-bandwidth memory # Where the complex architecture (programming) of GPUs/FPGAs is a barrier # Where the computation is something other than one for which dedicated accelerators exist (like GPU, DSPs, or a CPU's vector extensions, FP math etc) If most or all of those conditions hold, BitGrid could be a good architecture. Ideally, configured/programmed as if it were a simple memory array. No doubt such applications exist. But between those many competing options, I suspect BitGrid will have a hard time carving out a niche where its advantages weigh heavily enough to matter. Disclaimer: if nothing else, I would love it as a dead-simple-to-use-sea-of-LUTs. FPGAs are powerful but complicated beasts (and usually rely on closed source toolchains).