The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jul. 14, 2026

Filed:

Dec. 21, 2020
Applicant:

Northeastern University, Boston, MA (US);

Inventors:

Yanzhi Wang, Newton Highlands, MA (US);

Xue Lin, Newton Highlands, MA (US);

Assignee:

Northeastern University, Boston, MA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/082 (2023.01); G06N 3/063 (2023.01);
U.S. Cl.
CPC ...
G06N 3/082 (2013.01); G06N 3/063 (2013.01);
Abstract

ADMM-NN is an algorithm-hardware co-optimization framework of DNNs using Alternating Direction Method of Multipliers (ADMM). The first part of ADMM-NN is a systematic, joint framework of DNN weight pruning and quantization using ADMM. The second part is a hardware-aware optimization to facilitate hardware-level implementations. ADMM-based weight pruning and quantization accounts for (i) computation reduction and energy efficiency improvement and (ii) performance overhead due to irregular sparsity. Experimental results demonstrate that by combining weight pruning and quantization, the proposed framework can achieve 1,910× and 231× reductions in the overall model size on the LeNet-5 and AlexNet models. Favorable results are also observed on VGGNet and ResNet models. Also, without any accuracy loss, 3.6× reduction in the amount of computation can be achieved, outperforming prior work.


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