Generating power-optimal standard cell library specification using neural network technique

• Main Authors: Lim, Sze Huang, Lim, Yang Wei, Mashohor, Syamsiah, Kamsani, Noor Ain, Mohd Sidek, Roslina, Hashim, Shaiful Jahari, Rokhani, Fakhrul Zaman
• Format: Conference or Workshop Item
• Language: English
• Published: IEEE 2017
• Online Access: http://psasir.upm.edu.my/id/eprint/68267/1/Generating%20power-optimal%20standard%20cell%20library%20specification%20using%20neural%20network%20technique.pdf; http://psasir.upm.edu.my/id/eprint/68267/
• Institution: Universiti Putra Malaysia

In VLSI semi-custom design approach, power-optimal standard cell library selection for a given block design requires time-consuming iterative processes. This paper presents a framework to select a standard cell library that can result in near-optimal power while satisfying targeted frequency. The framework relies on neural network model to quickly predict the total power of a block design associated with a given standard cell library in order to speed up the synthesis process. The experimental result based on various synthesized benchmark circuits demonstrated the effectiveness of proposed framework for near-optimal standard cell library specification.