4 ms·
The document said it outputs the exact same values as the conventional method. There is no accuracy trade off here.
by waynecochran 3y ago
The document said it outputs the exact same values as the conventional method. There is no accuracy trade off here.
- p1esk 3y agoFor floating point? Are you sure?
- deleted 3y ago[deleted]
- waynecochran 3y agoOpening statement of README This repository contains the source code for ML hardware architectures that require nearly half the number of multiplier units to achieve the same performance, by executing alternative inner-product algorithms that trade nearly half the multiplications for cheap low-bitwidth additions, while still producing identical output as the conventional inner product.
- p1esk 3y agoI just looked at the paper: the answer is no, floating point is not supported.
- pclmulqdq 3y agoThe paper cited is about hardware, where there is no accuracy tradeoff because you control the numerical precision completely and use fixed point. In a software implementation, neither is true. There is no chance that you will get the exact same values out of this method that you do out of other FP matmuls.