39 citations · 119 across the 25 of their papers we have counts for
7 papers · 1 filter
GENIAL: Generative Design Space Exploration via Network Inversion for Low Power Algorithmic Logic Units
Maxence Bouvier, Ryan Amaudruz, Felix Arnold +2
As AI workloads proliferate, optimizing arithmetic units is becoming increasingly important for reducing the footprint of digital systems. Conventional design flows, which often re…
The Art of Beating the Odds with Predictor-Guided Random Design Space Exploration
Felix Arnold, Maxence Bouvier, Ryan Amaudruz +2
This work introduces an innovative method for improving combinational digital circuits through random exploration in MIG-based synthesis. High-quality circuits are crucial for perf…
Vau da muntanialas: Energy-efficient multi-die scalable acceleration of RNN inference
Gianna Paulin, Francesco Conti, Lukas Cavigelli +1
Recurrent neural networks such as Long Short-Term Memories (LSTMs) learn temporal dependencies by keeping an internal state, making them ideal for time-series problems such as spee…
Reinforcement Learning for Scalable Logic Optimization with Graph Neural Networks
Xavier Timoneda, Lukas Cavigelli
Logic optimization is an NP-hard problem commonly approached through hand-engineered heuristics. We propose to combine graph convolutional networks with reinforcement learning and…
ECG-TCN: Wearable Cardiac Arrhythmia Detection with a Temporal Convolutional Network
Thorir Mar Ingolfsson, Xiaying Wang, Michael Hersche +3
Personalized ubiquitous healthcare solutions require energy-efficient wearable platforms that provide an accurate classification of bio-signals while consuming low average power fo…
Sound Event Detection with Binary Neural Networks on Tightly Power-Constrained IoT Devices
Gianmarco Cerutti, Renzo Andri, Lukas Cavigelli +3
Sound event detection (SED) is a hot topic in consumer and smart city applications. Existing approaches based on Deep Neural Networks are very effective, but highly demanding in te…