Approximations in Deep Learning
Résumé
The design and implementation of Deep Learning (DL) models is currently receiving a lot of attention from both industrials and academics. However, the computational workload associated with DL is often out of reach for low-power embedded devices and is still costly when run on datacenters. By relaxing the need for fully precise operations, Approximate Computing (AxC) substantially improves performance and energy efficiency. DL is extremely relevant in this context, since playing with the accuracy needed to do adequate computations will significantly enhance performance, while keeping the quality of results in a user-constrained range. This chapter will explore how AxC can improve the performance and energy efficiency of hardware accelerators in DL applications during inference and training.
Mots clés
- Deep neural networks
- Low power
- High performance
- Low energy
- Support for approximation in DNN accelerators
- Floating-point arithmetic
- Fixed-point arithmetic
- Number representations
- Computer arithmetic
- Mixed precision training
- Low precision
- Binary neural network
- Weight sharing
- Quantization
- Pruning
- Approximation for training
- Approximation for inference
- Deep learning
- Approximate computing
| Origine | Fichiers produits par l'(les) auteur(s) |
|---|---|
| Licence |