Extended Data Fig. 6: PINN model quantization and performance.
From: A lossless and fully parallel spintronic compute-in-memory macro for artificial intelligence chips
Flow field reconstruction with PINN: A comparison of predictions for u (streamwise velocity), v (spanwise velocity), and p (pressure) at varying computational precision levels, with computational fluid dynamics (CFD) data benchmark32. (a) CFD benchmark data. (b) Predictions from the FP32 PINN model. (c) Predictions from the INT16 PINN model. (d) Predictions from the INT12 PINN model. (e) Predictions from the INT8 PINN model. (f) Predictions from the INT4 PINN model. (g) Relative L2 Norm (RL2): Temporal RL2 for u with different bit precisions and the overall RL2 for u. (h) Temporal RL2 for v with different bit precisions and the overall RL2 for v. (i) Temporal RL2 for u with different bit precisions and the overall RL2 for p.
