CEG4N: Counter-Example Guided Neural Network Quantization Refinement

Matos, João Batista P.; Bessa, Iury; Manino, Edoardo; Song, Xidan; Cordeiro, Lucas C.

doi:10.1007/978-3-031-21222-2_3

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite1

Independent0

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This paper extends a previous work [14], which tackles the problems of NN quantization and equivalence checking in a modular fashion within the CEG4N framework by iterating between two stages: searching for QNN candidates over a finite set of counterexamples and verifying the QNN to either prove equivalence or generate more counterexamples. Here, we present a number of additional contributions:…”

Section: Introductionmentioning

confidence: 82%

Counterexample Guided Neural Network Quantization Refinement

Matos,

de Lima Filho,

Bessa

et al. 2024

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

Self Cite

View full text Add to dashboard Cite

Deploying Neural networks (NNs) in low-resource domains is challenging because of their high computing, memory, and power requirements. For this reason, NNs are often quantized before deployment, but such an approach degrades their accuracy. Thus, we propose the counterexample guided neural network quantization refinement (CEG4N) framework, which combines search-based quantization and equivalence checking. The former minimizes computational requirements, while the latter guarantees that the behavior of an NN does not change after quantization. We evaluate CEG4N on a diverse set of benchmarks, including large and small NNs. Our technique successfully quantizes the networks in the chosen evaluation set, while producing models with up to 163% better accuracy than state-of-the-art techniques.

show abstract

Section: Introductionmentioning

confidence: 82%