Efficient Deep Learning in Communications

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1 Fraunhofer Image Processing Heinrich Hertz Institute Efficient Deep Learning in Communications Dr. Wojciech Samek Fraunhofer HHI, Machine Learning Group Fraunhofer Heinrich Hertz Institute, Einsteinufer 37, Berlin

2 Today s AI Systems AlphaGo beats Go human champ Deep Net outperforms humans in image classification DeepStack beats professional poker players Dermatologist-level classification of skin cancer with Deep Nets Computer out-plays humans in "doom" Revolutionizing Radiology with Deep Learning Deep Net beats human at recognizing traffic signs 2

3 Today s AI Systems Huge volumes of data Computing power Powerful models - Millions of labeled examples available - highly parallel processing - large power consumption (600 Watts per GPU card) - huge models (up to 137 billion parameters and 1001 layers) - architectures adapted to images, speech, text Communications settings are often different. 3

4 ML in Communications Satellite Communications Autonomous driving Smart Data Smartphones Internet of Things 5G Networks Many additional requirements: Small size, efficient execution, low energy consumption 4

5 ML in Communications Distributed setting Large nonstationarity Restricted ressources Communications costs Interoperability Security & privacy Interpretability We need ML techniques which are adapted to communications But it s not only the algorithms, also: - protocols - data formats - frameworks - mechanisms - Trustworthiness 5

6 Problem 1: Restricted ressources DNN with Millions of weight parameters - large size - energy-hungry training & inference - floating point operations Many recent work on compressing neural networks by weight quantization. 6

7 Problem 1: Restricted resources quantization (rate-distortion theory) compressed sparse row format - reduces storage - fast multiplications can we do better? 7

8 Problem 1: Restricted resources RD-theory based weight quantization does not necessarily lead to sparse matrices. Weight sharing property: Subsets of connections share the same weight value rewriting trick 8

9 Problem 1: Restricted resources more efficient format than CSR 9

10 Problem 1: Restricted resources iphone8 25 kj VGG-16 size: 553 MB, acc: %, ops: M, energy: 71 mj State-of-the-art Simple quantization compression (8 bit) + sparse format size: MB, MB, acc: acc: %, %, ops: ops: M, M, energy: energy: mj mj Sparse format State-of-the-art compression + WS format size: 773 (217) MB, acc: %, ops: M, energy: 65 mj size: 12.8 MB, acc: %, ops: 7225 M, energy: 16 mj WS format size: 247 (99) MB, acc: %, ops: M, energy: 36 mj 10

11 Problem 2: Interpretability Black Box verify system legal aspects learn new strategies understand weaknesses 11

12 Problem 2: Interpretability Black Box Theoretical interpretation: (Deep) Taylor decomposition of neural network 12

13 Problem 2: Interpretability Explanation? cat rooster dog 13

14 Problem 2: Interpretability what speaks for / against classification as 3 what speaks for / against classification as 9 14

15 Problem 2: Interpretability 15

16 Problem 2: Interpretability Predictions years old 60+ years old 16

17 Problem 2: Interpretability 17

18 Conclusion Bringing ML to communications comes with new challenges AI systems may behave differently than expected Need for best practices & recommendations (protocols, formats, ) 18

19 Thank you for your attention Questions??? All our papers available on: Acknowledgement Simon Wiedemann (HHI) Klaus-Robert Müller (TUB) Grégoire Montavon (TUB) Sebastian Lapuschkin (HHI) Leila Arras (HHI) 19

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