عنوان مقاله فارسی: طراحی شتاب دهنده کم مصرف برای شبکه های عصبی با استفاده مجدد از محاسبات
عنوان مقاله لاتین: Power-Efficient Accelerator Design for Neural Networks Using Computation Reuse
نویسندگان: Ali Yasoubi; Reza Hojabr; Mehdi Modarressi
تعداد صفحات: 3
سال انتشار: 2017
زبان: لاتین
Abstract:
Applications of neural networks in various fields of research and technology have expanded widely in recent years. In particular, applications with inherent tolerance to accuracy loss, such as signal processing and multimedia applications, are highly suited to the approximation property of neural networks. This approximation property has been exploited in many existing neural network accelerators to trade-off accuracy for power-efficiency and speed. In addition to the power saving obtained by approximation, we observed that a considerable amount of arithmetic operations in neural networks are repetitive and can be eliminated to further decrease power consumption. Given this observation, we propose CORN, COmputation Reuse-aware Neural network accelerator that allows neurons to share their computation results, effectively eliminating the power usage of redundant computations. We will show that CORN lowers power consumption by 26 percent on average over low-power neural network accelerators.
power-efficient accelerator design for neural networks using computation reuse_1619529603_47946_4145_1836.zip0.22 MB |