Calculator Form
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Example Data Table
This sample shows typical activation values before quantization.
| Sample | Original Value | Suggested Range | Bit Depth | Mode |
|---|---|---|---|---|
| 1 | -1.25 | -1.50 to 1.50 | 8 | Symmetric Signed |
| 2 | -0.92 | -1.50 to 1.50 | 8 | Symmetric Signed |
| 3 | 0.14 | -1.50 to 1.50 | 8 | Symmetric Signed |
| 4 | 0.79 | -1.50 to 1.50 | 8 | Symmetric Signed |
| 5 | 1.46 | -1.50 to 1.50 | 8 | Symmetric Signed |
Formula Used
Symmetric signed:
scale = max(|real_min|, |real_max|) / q_maxAsymmetric:
scale = (real_max - real_min) / (q_max - q_min)
Symmetric signed usually fixes
zero_point = 0.Asymmetric uses
zero_point = round(q_min - real_min / scale).
scaled = x / scale + zero_pointq = clamp(round(scaled), q_min, q_max)
x_hat = (q - zero_point) × scale
error = x - x_hatMSE = average(error²)RMSE = sqrt(MSE)MAE = average(|error|)SQNR = 10 × log10(sum(x²) / sum(error²))
How to Use This Calculator
- Enter a tensor name so results stay easy to identify.
- Choose the target bit depth for quantized storage.
- Select symmetric or asymmetric integer mapping.
- Pick a rounding rule that matches your deployment flow.
- Choose custom range values or derive them from samples.
- Paste sample values from activations or model weights.
- Press the calculate button to generate metrics and graphs.
- Download CSV or PDF if you need reports or audits.
FAQs
1) What does quantization error mean?
Quantization error is the difference between the original floating-point value and its reconstructed value after integer mapping and dequantization. It reflects precision loss introduced by compression.
2) Why is bit depth important?
Bit depth controls how many integer levels are available. Lower depths reduce memory and speed up inference, but they usually increase rounding error and clipping risk.
3) When should I use symmetric quantization?
Symmetric quantization works well when data is centered near zero, especially for weights. It keeps zero-point simple and often suits hardware pipelines that prefer zero-centered integers.
4) When is asymmetric quantization better?
Asymmetric quantization is useful when data is not centered around zero. It can preserve more usable resolution for skewed activation ranges by shifting the integer mapping.
5) What does clipping tell me?
Clipping means some values exceeded the representable quantized range. High clipping rates often indicate poor range selection and usually damage downstream model accuracy more than small rounding noise.
6) Which metric should I trust most?
Use several metrics together. MSE and RMSE punish large errors, MAE is intuitive, and SQNR shows signal preservation. The best choice depends on your deployment objective.
7) Can this help compare calibration strategies?
Yes. Run the same tensor under different ranges, modes, and bit depths. Then compare clip rate, SQNR, and reconstruction error to judge calibration quality.
8) Does lower error guarantee better model accuracy?
Not always. Lower tensor error often helps, but final model accuracy also depends on layer sensitivity, calibration data quality, operator support, and accumulation behavior.