Is it same as Quantization?

Quantization and binarization are both techniques used to reduce the precision of model parameters, but they differ in their approach and the level of precision reduction.

Quantization:

1. Precision Reduction: Quantization reduces the precision of model parameters from high precision (e.g., 32-bit floating-point) to lower precision (e.g., 8-bit integer).
2. Multiple Levels: Typically, quantization involves using multiple levels of precision, such as 8-bit, 16-bit, or even 4-bit, to represent model parameters.
3. Arithmetic: Arithmetic operations are performed using fixed-point or integer arithmetic with the quantized parameters.
4. Example: Representing a weight originally stored in 32 bits as an 8-bit integer.

Binarization (1-bit Quantization):

1. Extreme Reduction: Binarization, also known as 1-bit quantization, is an extreme form of quantization where model parameters are reduced to just 1 bit, typically represented as either -1 or +1.
2. Binary Representation: Each parameter is mapped to one of two possible values, effectively representing a binary decision.
3. Arithmetic: Arithmetic operations become binary operations, where multiplications are reduced to simple sign changes and additions become exclusive OR (XOR) operations.
4. Example: Converting a weight originally stored in 32 bits to just 1 bit by setting it to either -1 or +1 based on a threshold.

Differences:

1. Precision Levels: Quantization offers multiple levels of precision, whereas binarization reduces precision to the extreme, using only 1 bit per parameter.
2. Arithmetic Operations: Quantization involves performing operations with reduced precision numbers (e.g., integers), while binarization simplifies arithmetic to binary operations (e.g., sign changes, XOR).
3. Memory and Computational Requirements: Binarization achieves the most significant reduction in memory and computational requirements due to its extreme precision reduction compared to traditional quantization methods.
4. Accuracy Impact: Binarization often leads to a more significant loss in model accuracy compared to traditional quantization methods due to the extreme precision reduction.

Practical Considerations:

• Quantization: Suitable for scenarios where a balance between model size, performance, and accuracy is required, such as deploying models on resource-constrained devices.
• Binarization: Used in extreme cases where minimizing memory and computational requirements is critical, even at the cost of significant accuracy degradation.

Conclusion:

While both quantization and binarization aim to reduce the precision of model parameters, binarization takes this reduction to the extreme by representing parameters with just 1 bit. This extreme reduction offers unparalleled memory and computational efficiency but often comes at the cost of significant accuracy loss, making it suitable for only specific use cases where efficiency is paramount.