论文标题
深度学习的近似
Approximations in Deep Learning
论文作者
论文摘要
深度学习（DL）模型的设计和实施目前正在引起工业和学者的广泛关注。但是，与DL相关的计算工作负载通常无法触及低功率嵌入式设备，并且在数据中心运行时仍然是昂贵的。通过放松对完全精确的操作的需求，近似计算（AXC）可以显着提高性能和能源效率。在这种情况下，DL非常相关，因为进行适当计算所需的准确性将大大提高性能，同时将结果质量保持在用户约束范围内。本章将探讨AXC如何在推理和培训期间在DL应用中提高硬件加速器的性能和能源效率。
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.
