Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2023

Abstract

To alleviate the practical constraints for deploying deep neural networks (DNNs) on edge devices, quantization is widely regarded as one promising technique. It reduces the resource requirements for computational power and storage space by quantizing the weights and/or activation tensors of a DNN into lower bit-width fixed-point numbers, resulting in quantized neural networks (QNNs). While it has been empirically shown to introduce minor accuracy loss, critical verified properties of a DNN might become invalid once quantized. Existing verification methods focus on either individual neural networks (DNNs or QNNs) or quantization error bound for partial quantization. In this work, we propose a quantization error bound verification method, named QEBVerif, where both weights and activation tensors are quantized. QEBVerif consists of two parts, i.e., a differential reachability analysis (DRA) and a mixed-integer linear programming (MILP) based verification method. DRA performs difference analysis between the DNN and its quantized counterpart layer-by-layer to compute a tight quantization error interval efficiently. If DRA fails to prove the error bound, then we encode the verification problem into an equivalent MILP problem which can be solved by off-the-shelf solvers. Thus, QEBVerif is sound, complete, and reasonably efficient. We implement QEBVerif and conduct extensive experiments, showing its effectiveness and efficiency.

Discipline

OS and Networks | Software Engineering

Research Areas

Information Systems and Management

Publication

Proceedings of the 35th International Conference on Computer Aided Verification, Paris, France, 2023 July 17-22

First Page

413

Last Page

437

ISBN

9783031377020

Identifier

10.1007/978-3-031-37703-7_20

Publisher

Springer

City or Country

Cham

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/978-3-031-37703-7_20

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