137 citations · 511 across the 22 of their papers we have counts for
8 papers · 1 filter
Approximate Computing and the Efficient Machine Learning Expedition
Jörg Henkel, Hai Li, Anand Raghunathan +4
Approximate computing (AxC) has been long accepted as a design alternative for efficient system implementation at the cost of relaxed accuracy requirements. Despite the AxC researc…
Energy-efficient DNN Inference on Approximate Accelerators Through Formal Property Exploration
Ourania Spantidi, Georgios Zervakis, Iraklis Anagnostopoulos +1
Deep Neural Networks (DNNs) are being heavily utilized in modern applications and are putting energy-constraint devices to the test. To bypass high energy consumption issues, appro…
Agent-based Constraint Solving for Resource Allocation in Manycore Systems
Volker Wenzel, Lars Bauer, Wolfgang Schröder-Preikschat +1
For efficiency reasons, manycore systems are increasingly heterogeneous, which makes the mapping of complex workloads a key problem with a high optimization potential. Constraints…
Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey
Giorgos Armeniakos, Georgios Zervakis, Dimitrios Soudris +1
Deep Neural Networks (DNNs) are very popular because of their high performance in various cognitive tasks in Machine Learning (ML). Recent advancements in DNNs have brought beyond…
Automated Design Approximation to Overcome Circuit Aging
Konstantinos Balaskas, Georgios Zervakis, Hussam Amrouch +2
Transistor aging phenomena manifest themselves as degradations in the main electrical characteristics of transistors. Over time, they result in a significant increase of cell propa…
Cross-Layer Approximation For Printed Machine Learning Circuits
Giorgos Armeniakos, Georgios Zervakis, Dimitrios Soudris +2
Printed electronics (PE) feature low non-recurring engineering costs and low per unit-area fabrication costs, enabling thus extremely low-cost and on-demand hardware. Such low-cost…
CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization
Kilian Pfeiffer, Martin Rapp, Ramin Khalili +1
Devices participating in federated learning (FL) typically have heterogeneous communication, computation, and memory resources. However, in synchronous FL, all devices need to fini…
AdaPT: Fast Emulation of Approximate DNN Accelerators in PyTorch
Dimitrios Danopoulos, Georgios Zervakis, Kostas Siozios +2
Current state-of-the-art employs approximate multipliers to address the highly increased power demands of DNN accelerators. However, evaluating the accuracy of approximate DNNs is…