| | --- |
| | license: mit |
| | tags: |
| | - pytorch |
| | - safetensors |
| | - threshold-logic |
| | - neuromorphic |
| | --- |
| | |
| | # threshold-clz4 |
| |
|
| | 4-bit count leading zeros. |
| |
|
| | ## Function |
| |
|
| | clz4(a3, a2, a1, a0) = number of leading zeros from MSB |
| |
|
| | ## Truth Table (selected rows) |
| |
|
| | | Input | First 1 | CLZ | Output | |
| | |-------|---------|-----|--------| |
| | | 1xxx | bit 3 | 0 | 000 | |
| | | 01xx | bit 2 | 1 | 001 | |
| | | 001x | bit 1 | 2 | 010 | |
| | | 0001 | bit 0 | 3 | 011 | |
| | | 0000 | none | 4 | 100 | |
| |
|
| | ## Architecture |
| |
|
| | ``` |
| | Layer 1: Priority detection from MSB |
| | has3 = a3 (MSB is set, clz=0) |
| | has2_first = a2 AND NOT(a3) (clz=1) |
| | has1_first = a1 AND NOT(a2) AND NOT(a3) (clz=2) |
| | has0_first = a0 AND NOT(a1) AND NOT(a2) AND NOT(a3) (clz=3) |
| | all_zero = NOT(any bit) (clz=4) |
| | |
| | Layer 2: Binary encoding |
| | y0 = has2_first OR has0_first (clz is 1 or 3) |
| | y1 = has1_first OR has0_first (clz is 2 or 3) |
| | y2 = all_zero (clz is 4) |
| | ``` |
| |
|
| | ## Parameters |
| |
|
| | | | | |
| | |---|---| |
| | | Inputs | 4 | |
| | | Outputs | 3 | |
| | | Neurons | 8 | |
| | | Layers | 2 | |
| | | Parameters | 39 | |
| | | Magnitude | 26 | |
| |
|
| | ## Usage |
| |
|
| | ```python |
| | from safetensors.torch import load_file |
| | # See model.py for full implementation |
| | |
| | # clz4(1, 0, 0, 0) = [0, 0, 0] = 0 (MSB is set) |
| | # clz4(0, 0, 1, 0) = [0, 1, 0] = 2 (two leading zeros) |
| | # clz4(0, 0, 0, 0) = [1, 0, 0] = 4 (all zeros) |
| | ``` |
| |
|
| | ## License |
| |
|
| | MIT |
| |
|
