Approximate Computing and the Efficient Machine Learning Expedition

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 research activities in various application domains, AxC thrived the past decade when it was applied in Machine Learning (ML). T...

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Veröffentlicht in:2022 IEEE/ACM International Conference On Computer Aided Design (ICCAD) S. 1 - 9
Hauptverfasser: Henkel, Jorg, Li, Hai, Raghunathan, Anand, Tahoori, Mehdi B., Venkataramani, Swagath, Yang, Xiaoxuan, Zervakis, Georgios
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: ACM 29.10.2022
Schlagworte:
Approximate computing
Computational modeling
Costs
Design automation
In-memory
Machine learning
Precision Scaling
Printed Electronics
Prototypes
Pruning
Quantization
Taxonomy
Transformers
ISSN:1558-2434
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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 research activities in various application domains, AxC thrived the past decade when it was applied in Machine Learning (ML). The by definition approximate notion of ML models but also the increased computational overheads associated with ML applications-that were effectively mitigated by corresponding approximations-led to a perfect matching and a fruitful synergy. AxC for AI/ML has transcended beyond academic prototypes. In this work, we enlighten the synergistic nature of AxC and ML and elucidate the impact of AxC in designing efficient ML systems. To that end, we present an overview and taxonomy of AxC for ML and use two descriptive application scenarios to demonstrate how AxC boosts the efficiency of ML systems.
ISSN:1558-2434
DOI:10.1145/3508352.3561105