Method and apparatus for quantization, adaptive block partitioning and codebook coding for neural network model compression

An apparatus for quantization, adaptive block partitioning and codebook coding for neural network model compression, the apparatus comprising:

• at least one memory configured to store program code;
• and
• at least one processor configured to read the program code and operate as instructed by the program code, the program code comprising:
• first determining code configured to cause the at least one processor to determine a saturated maximum value of a multi-dimensional tensor in a layer of a neural network, and a bit depth corresponding to the saturated maximum value;
• clipping code configured to cause the at least one processor to clip weight coefficients in the multi-dimensional tensor to be within a range of the saturated maximum value;
• quantizing code configured to cause the at least one processor to quantize the clipped weight coefficients, based on the bit depth;
• transmitting code configured to cause the at least one processor to transmit, to a decoder, a layer header comprising the bit depth and a plurality of partitioning parameters that specify at least a height threshold and a width threshold;
• reshaping code configured to cause the at least one processor to reshape a four-dimensional (4D) parameter tensor of a neural network, among the quantized weight coefficients, into a three-dimensional (3D) parameter tensor of the neural network, the 3D parameter tensor comprising a convolution kernel size, an input feature size and an output feature size;
• and
• partitioning the 3D parameter tensor based on the height threshold and the width threshold.

A non-transitory computer-readable medium storing instructions that, when executed by at least one processor for quantization, adaptive block partitioning and codebook coding for neural network model compression, cause the at least one processor to:

• determine a saturated maximum value of a multi-dimensional tensor in a layer of a neural network, and a bit depth corresponding to the saturated maximum value;
• clip weight coefficients in the multi-dimensional tensor to be within a range of the saturated maximum value;
• quantize the clipped weight coefficients, based on the bit depth;
• transmit, to a decoder, a layer header comprising the bit depth and a plurality of partitioning parameters that specify at least a height threshold and a width threshold;
• reshape a four-dimensional (4D) parameter tensor of a neural network, among the quantized weight coefficients, into a three-dimensional (3D) parameter tensor of the neural network, the 3D parameter tensor comprising a convolution kernel size, an input feature size and an output feature size;
• and
• partitioning the 3D parameter tensor based on the height threshold and the width threshold.