78 citations · 184 across the 24 of their papers we have counts for
6 papers · 1 filter
Explicit Sign-Magnitude Encoders Enable Power-Efficient Multipliers
Felix Arnold, Maxence Bouvier, Ryan Amaudruz +2
This work presents a method to maximize power-efficiency of fixed point multiplier units by decomposing them into sub-components. First, an encoder block converts the operands from…
Deep Recommender Models Inference: Automatic Asymmetric Data Flow Optimization
Giuseppe Ruggeri, Renzo Andri, Daniele Jahier Pagliari +1
Deep Recommender Models (DLRMs) inference is a fundamental AI workload accounting for more than 79% of the total AI workload in Meta's data centers. DLRMs' performance bottleneck i…
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…
FlatAttention: Dataflow and Fabric Collectives Co-Optimization for Efficient Multi-Head Attention on Tile-Based Many-PE Accelerators
Chi Zhang, Luca Colagrande, Renzo Andri +6
Multi-Head Attention (MHA) is a critical computational kernel in transformer-based AI models. Emerging scalable tile-based accelerator architectures integrate increasing numbers of…
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…
Top-Theta Attention: Sparsifying Transformers by Compensated Thresholding
Konstantin Berestizshevsky, Renzo Andri, Lukas Cavigelli
We present Top-Theta (Top-) Attention, a training-free method for sparsifying transformer attention during inference. Our key insight is that static, per-head thresholds can be…