IMHO, for fixed-point MM accelerators, there is no catch, I think it's an overlooked algorithm. It's based on an algorithm by Winograd who coincidentally also proposed another unrelated algorithm that later became very popular for CNN acceleration which would take some visibility away from this other algorithm by Winograd... But that is speculative
On the other hand, if you tried it with floating point, you'd lose significant digits. Since the approach is to sum (a[i] + b[i+1])(a[i+1] + b[i]) and subtract the sums of a[i]a[i+1] and b[i]b[i+1] in the end to get a[i]b[i] + a[i+1]b[i+1], you may be taking the difference of two large values to get a small value, losing precision.
LLM hype and this submission in particular keep making me think of a lecturer I had for Topics in Large Dimensional Data Processing, circa 2016: as I recall he was enthusiastically adamant that the most important thing, breakthroughs etc., in years/decades to come was going to be faster matrix operations. Anyway, I'm pretty sure I recognise FIP (not FFIP of course) from that course.
I wish I could remember his name, I believe he left academia after my year and went to work in industry, I'd just be curious to see what he's up to now. I'm not saying it was a particularly novel or prescient comment/attitude, we may not have had quite such ML hype but certainly 'big data' was all the rage at the time, it's just something that's stuck in my mind. One of those areas I always meant to study more, just realistically probably never had the mathematical chops for and certainly those I did have atrophied.
Maybe I’m joking, but: our society is just a vehicle for economics at this point, our economy is built around science, our science has mostly been turned into observations about engineering, some time ago we changed all of engineering into differential equations, and differential equations can be solved by discretizing them and doing linear algebra, and most of linear algebra can be done with matrix multiplications (triangular solves and orthonormalizations if you are fancy). All you need is matmul.
> our science has mostly been turned into observations about engineering
You may be joking but that in particular seems pretty astute.
Superficially it seems accurate, and reasonably ascribable to economic forces, fewer concentrations of capital in people (aristocrats) spending it on a hobby interest or academic pursuit of their own - today's equivalents mostly prefer philanthropy (Musk is, I suppose, for whatever else you might think of him, a notable exception - preferring to explore space, AI, etc. not really it seems for personal monetary gain). But I wonder if that is fair, to modern scientists, or is it just that 'all the low-hanging stuff's been done'?
For life sciences need grad students / postdocs to do the grunt work of pipetting, dissected, plating etc. And whatever the equivalent is in chemistry (titration/GC/mass transfer I guess)?
But those tools created by engineers are pretty darn important, and allow plenty of experiments/observations to be performed that were previously out of reach.
I don't think so, this may be the case in your country of course. I may well have recorded it in my notes if I dig them out, but this was a fourth year course and they certainly degraded over the years.
