Project Details

AppNeCo: Aproximativní neurovýpočty

Project Period: 1. 1. 2022 - 31. 12. 2024

Project Type: grant

Code: GA22-02067S

Agency: Czech Science Foundation

Program: Standardní projekty

English title
AppNeCo: Approximate Neurocomputing

approximate computing,convolutional networks,energy complexity,robust learning,hardware accelerator,image classification


Nowadays, modern AI technologies based on deep neural networks, whose computation is demanding on energy consumption, are implemented in devices with limited resources (e.g. battery powered cellphones). In error-tolerant applications (e.g. image classification), the use of approximate computing methods can save enormous amount of energy at the cost of only a small loss in accuracy. AppNeCo is a basic research project of approximate neurocomputing, whose ambition is an original synergy of approximation and complexity theory of neural networks and empirical experience with the top design of high-performance approximate implementations of hardware circuits. Its goal is to develop complexity-theoretic foundations of approximate computation by convolutional neural networks (CNN) of bounded energy complexity for application domains specified by input space distributions. This knowledge will be used in designing new strategies for approximating components and learning algorithms of low-energy high-precision CNNs. The new methods will be tested on image processing tasks.

Team members
Sekanina Lukáš, prof. Ing., Ph.D. (UPSY FIT VUT) , research leader
Klhůfek Jan, Ing. (UPSY FIT VUT)
Mrázek Vojtěch, Ing., Ph.D. (UPSY FIT VUT)
Vašíček Zdeněk, doc. Ing., Ph.D. (UPSY FIT VUT)



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