Apparatuses and methods of manufacturing same, systems, and methods for performing network parameter quantization in deep neural networks are described. In one aspect, multi-dimensional vectors representing network parameters are constructed from a trained neural network model. The multi-dimensional vectors are quantized to obtain shared quantized vectors as cluster centers, which are fine-tuned. The fine-tuned and shared quantized vectors/cluster centers are then encoded. Decoding reverses the process.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for network quantization using multi-dimensional vectors, comprising: constructing multi-dimensional vectors representing network parameters from a trained neural network model; quantizing the multi-dimensional vectors to obtain shared quantized vectors as cluster centers; fine-tuning the shared quantized vectors/cluster centers; and encoding using the shared quantized vectors/cluster centers. 2. The method of claim 1, wherein encoding comprises: creating binary codewords using entropy coding on the shared quantized vectors/cluster centers. 3. The method of claim 1, wherein quantizing the multi-dimensional vectors comprises: using lattice quantization to quantize the multi-dimensional vectors. 4. The method of claim 1, wherein quantizing the multi-dimensional vectors comprises: using an iterative algorithm to solve an entropy-constrained vector quantization. 5. The method of claim 1, wherein quantizing the multi-dimensional vectors comprises: using universal quantization to quantize the multi-dimensional vectors. 6. The method of claim 5, further comprising: randomizing the network parameters using dithering before quantizing. 7. The method of claim 6, wherein randomizing the network parameters using dithering comprises: using a pseudo-random number generator to generate random dithering values. 8. The method of claim 1, wherein quantizing the multi-dimensional vectors comprises: converting the quantized vectors into symbols. 9. The method of claim 8, wherein converting the quantized vectors into symbols comprises: using a lookup table to find symbols corresponding to the quantized vectors. 10. The method of claim 8, wherein encoding comprises: using universal coding. 11. An apparatus, comprising: one or more non-transitory computer-readable media; and at least one processor which, when executing instructions stored on one or more non-transitory computer readable media, performs the steps of: constructing multi-dimensional vectors representing network parameters from a trained neural network model; quantizing the multi-dimensional vectors to obtain shared quantized vectors as cluster centers; fine-tuning the shared quantized vectors/cluster centers; and encoding using the shared quantized vectors/cluster centers. 12. The apparatus of claim 11, wherein the at least one processor performs encoding by: creating binary codewords using entropy coding on the shared quantized vectors/cluster centers. 13. The apparatus of claim 11, wherein the at least one processor performs quantizing the multi-dimensional vectors by at least: using lattice quantization to quantize the multi-dimensional vectors. 14. The apparatus of claim 11, wherein the at least one processor performs quantizing the multi-dimensional vectors by: using an iterative algorithm to solve an entropy-constrained vector quantization. 15. The apparatus of claim 11, wherein the at least one processor performs quantizing the multi-dimensional vectors by: using universal quantization to quantize the multi-dimensional vectors. 16. The apparatus of claim 11, wherein the at least one processor further performs the step of: randomizing the network parameters using dithering before quantizing. 17. The apparatus of claim 16, wherein the at least one processor performs randomizing the network parameters using dithering by: using a pseudo-random number generator to generate random dithering values. 18. The apparatus of claim 16, wherein the at least one processor performs converting the quantized vectors into symbols by: using a lookup table to find symbols corresponding to the quantized vectors. 19. A method, comprising: manufacturing a chipset comprising: at least one processor which, when executing instructions stored on one or more non-transitory computer readable media, performs the steps of: constructing multi-dimensional vectors representing network parameters from a trained neural network model; quantizing the multi-dimensional vectors to obtain shared quantized vectors as cluster centers; fine-tuning the shared quantized vectors/cluster centers; and encoding using the shared quantized vectors/cluster centers; and the one or more non-transitory computer-readable media which store the instructions. 20. A method of testing an apparatus, comprising: testing whether the apparatus has at least one processor which, when executing instructions stored on one or more non-transitory computer readable media, performs the steps of: constructing multi-dimensional vectors representing network parameters from a trained neural network model; quantizing the multi-dimensional vectors to obtain shared quantized vectors as cluster centers; fine-tuning the shared quantized vectors/cluster centers; and encoding using the shared quantized vectors/cluster centers; and testing whether the apparatus has the one or more non-transitory computer-readable media which store the instructions.