Brain Bit Precision Int32 FP32, Int16 PF16, Int8 FP8, Int6 FP6, Int4? Idealness of Computational Machine Learning ML TOPS for the human brain:
Brain level Int/Float inferencing is ideally in Int8/7 with error bits or float remainders
Comparison List : RS
48Bit Int+Float Int48+FP48 (many connections, Eyes for example) HDR Vison
40BitInt+Float Int40+FP40 HDR Basic
Int8 Float16(2Channel, Brain Node)(3% Brain Study)
Int7 (20% Brain Study)
Int6 (80% Brain Study)
Int5 (Wolves (some are 6+))
Int4 (Sheep & worms)
Int3 (Germ biosystems)
Statistically a science test stated 80% of brains in man quantify Bit at 6 20% to 7Bit
XBox X & PlayStation 5 do down to INT4Bit (quite likely for quick inferencing)
Be aware that using 4 bit Int instructions .. potentially means more instructions used per clock cycle & more micro data transfers..
Int8 is most commonly liable to quantify data with minimum error in 8Bit like the Atari STE or the Nintendo 8Bit..
Colour perception for example is many orders of magnitude higher! Or 8bit colours EGA is all we would use..
16Bit was not good enough.. But 32Bit suites most people! But 10Bit(x4) 40Bit & Dolby 12Bit(x4) 48Bit is a luxury & we love it!
(c)Rupert S https://is.gd/ProcessorLasso
Restricted Boltzmann ML Networks : Brain Efficient
For example this works well with fonts & web browsers & consoles or standard input display hubs or User Interfaces, UI & JS & Webpage code.
In the old days photo applications did exist to use ML Image enhancement on older processors..
So how do they exploit Machine Learning on hardware with MMX for example ?
Procedural process data analytics:
Converting large statistics data bases; On general Tessellation/Interpolation of images
The procedural element is writing the code that interpolates data based upon the statistics database...
Linearity or curvature...
Association of grain & texture...
Databases get large fast & a 2 MB to 15MB Database makes the most sense...
Averages have to be categorized by either being worthy of 2 Places in the database or an average..
You can still run ML on a database object & then the points in the table are called nodes!
Indeed you can do both, However database conversion makes datasets way more manageable to run within the SiMD & AVX feature-set.
However the matter of inferencing then has to be reduced to statistical averages & sometimes ML runs fine inferencing this way.
Both ways work, Whatever is best for you & the specific hardware.
DL-ML slide : Machine Learning DL-ML