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Raspberry Pi Pico (RP2040) and Adafruit Metro M7 (NXP IMXRT10XX) benchmark

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circuitpython benchmark

Raspberry Pi Pico (RP2040), Adafruit Metro M7 (NXP IMXRT10XX) and Intel i7 CPU benchmark.

abstract

Script for Python and CircuitPython measures computation time, covering:

  • int and float datatypes
  • vectorized vs. looped operations
  • arithmetic, algebraic and trigonometric operations.

results


datatype

operation
Raspberry Pi Pico
t (s)
Adafruit Metro M7
t (s)

vs. Pico
Intel i7-6700HQ Laptop
t (s)

vs. Pico
int bitshift 31.614 6.710 4.7 x 0.164 192.5 x
int modulo 12.077 2.404 5.0 x 0.147 82.0 x
int bitwise-and 11.597 2.313 5.0 x 0.145 80.1 x
int bitwise-or 11.596 2.314 5.0 x 0.152 76.5 x
int bitwise-xor 11.598 2.312 5.0 x 0.152 76.4 x
int add 19.694 4.051 4.9 x 0.152 129.2 x
float add 13.319 2.629 5.1 x 0.127 105.1 x
array(np.float) add 1.561 0.183 8.5 x 0.006 248.7 x
vec speedup 8.5 x 14.4 x 20.2 x
int sub 11.597 2.313 5.0 x 0.147 79.1 x
float sub 13.566 2.633 5.2 x 0.132 102.5 x
array(np.float) sub 1.754 0.179 9.8 x 0.006 272.3 x
vec speedup 7.7 x 14.7 x 20.5 x
int mul 34.808 6.549 5.3 x 0.144 241.6 x
float mul 13.383 2.639 5.1 x 0.135 98.9 x
array(np.float) mul 1.890 0.204 9.3 x 0.006 311.1 x
vec speedup 7.1 x 12.9 x 22.3 x
int div 13.549 2.381 5.7 x 0.138 97.9 x
float div 13.991 2.692 5.2 x 0.125 111.5 x
array(np.float) div 2.097 0.238 8.8 x 0.006 340.9 x
vec speedup 6.7 x 11.3 x 20.4 x
int exp 20.625 3.221 6.4 x 0.297 69.4 x
float exp 20.457 3.547 5.8 x 0.285 71.7 x
array(np.float) exp 6.627 0.510 13.0 x 0.012 548.1 x
vec speedup 3.1 x 7.0 x 23.6 x
int sqr 17.391 3.241 5.4 x 0.288 60.4 x
float sqr 17.134 3.301 5.2 x 0.276 62.2 x
array(np.float) sqr 2.844 1.017 2.8 x 0.006 445.9 x
vec speedup 6.0 x 3.2 x 43.2 x
float sin 23.491 3.550 6.6 x 0.281 83.7 x
array(np.float) sin 6.638 0.605 11.0 x 0.015 451.7 x
vec speedup 3.5 x 5.9 x 19.1 x
float cos 20.729 3.539 5.9 x 0.299 69.3 x
array(np.float) cos 6.625 0.603 11.0 x 0.014 467.0 x
vec speedup 3.1 x 5.9 x 21.1 x
float tan 21.281 3.682 5.8 x 0.287 74.0 x
array(np.float) tan 7.151 0.768 9.3 x 0.025 290.2 x
vec speedup 3.0 x 4.8 x 11.7 x
float log 23.290 3.514 6.6 x 0.331 70.3 x
array(np.float) log 8.475 0.625 13.6 x 0.012 724.0 x
vec speedup 2.7 x 5.6 x 28.3 x
for(int) matmul 9.812 2.049 4.8 x 0.274 35.9 x
M(np.int16) matmul 1.146 0.045 25.5 x 0.001 1200.4 x
vec speedup 8.6 x 45.6 x 286.5 x
for(float) matmul 11.020 2.632 4.2 x 0.253 43.6 x
M(np.float) matmul 0.802 0.041 19.6 x 0.000 1914.1 x
vec speedup 13.7 x 64.2 x 603.6 x

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