Bespoke Approximation of Multiplication-Accumulation and Activation Targeting Printed Multilayer Perceptrons

1 code implementation • 29 Dec 2023 • Florentia Afentaki, Gurol Saglam, Argyris Kokkinis, Kostas Siozios, Georgios Zervakis, Mehdi B Tahoori

Printed Electronics (PE) feature distinct and remarkable characteristics that make them a prominent technology for achieving true ubiquitous computing.

Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization

no code implementations • 23 Dec 2023 • Konstantinos Balaskas, Andreas Karatzas, Christos Sad, Kostas Siozios, Iraklis Anagnostopoulos, Georgios Zervakis, Jörg Henkel

We explore, for the first time, per-layer fine- and coarse-grained pruning, in the same DNN architecture, in addition to low bit-width mixed-precision quantization for weights and activations.

Quantization Reinforcement Learning (RL)

Approximate Decision Trees For Machine Learning Classification on Tiny Printed Circuits

no code implementations • 15 Mar 2022 • Konstantinos Balaskas, Georgios Zervakis, Kostas Siozios, Mehdi B. Tahoori, Joerg Henkel

Although Printed Electronics (PE) cannot compete with silicon-based systems in conventional evaluation metrics, e. g., integration density, area and performance, PE offers attractive properties such as on-demand ultra-low-cost fabrication, flexibility and non-toxicity.

BIG-bench Machine Learning

AdaPT: Fast Emulation of Approximate DNN Accelerators in PyTorch

1 code implementation • 8 Mar 2022 • Dimitrios Danopoulos, Georgios Zervakis, Kostas Siozios, Dimitrios Soudris, Jörg Henkel

Current state-of-the-art employs approximate multipliers to address the highly increased power demands of DNN accelerators.