2 research outputs found
Deep Learning Training with Simulated Approximate Multipliers
This paper presents by simulation how approximate multipliers can be utilized
to enhance the training performance of convolutional neural networks (CNNs).
Approximate multipliers have significantly better performance in terms of
speed, power, and area compared to exact multipliers. However, approximate
multipliers have an inaccuracy which is defined in terms of the Mean Relative
Error (MRE). To assess the applicability of approximate multipliers in
enhancing CNN training performance, a simulation for the impact of approximate
multipliers error on CNN training is presented. The paper demonstrates that
using approximate multipliers for CNN training can significantly enhance the
performance in terms of speed, power, and area at the cost of a small negative
impact on the achieved accuracy. Additionally, the paper proposes a hybrid
training method which mitigates this negative impact on the accuracy. Using the
proposed hybrid method, the training can start using approximate multipliers
then switches to exact multipliers for the last few epochs. Using this method,
the performance benefits of approximate multipliers in terms of speed, power,
and area can be attained for a large portion of the training stage. On the
other hand, the negative impact on the accuracy is diminished by using the
exact multipliers for the last epochs of training.Comment: Presented at: IEEE International Conference on Robotics and
Biomimetics (ROBIO) 2019, Dali, China, December 2019. WINNER OF THE MOZI BEST
PAPER IN AI AWAR
A Survey on Approximate Multiplier Designs for Energy Efficiency: From Algorithms to Circuits
Given the stringent requirements of energy efficiency for Internet-of-Things
edge devices, approximate multipliers, as a basic component of many processors
and accelerators, have been constantly proposed and studied for decades,
especially in error-resilient applications. The computation error and energy
efficiency largely depend on how and where the approximation is introduced into
a design. Thus, this article aims to provide a comprehensive review of the
approximation techniques in multiplier designs ranging from algorithms and
architectures to circuits. We have implemented representative approximate
multiplier designs in each category to understand the impact of the design
techniques on accuracy and efficiency. The designs can then be effectively
deployed in high-level applications, such as machine learning, to gain energy
efficiency at the cost of slight accuracy loss.Comment: 38 pages, 37 figure