AutoCkt: Deep Reinforcement Learning of Analog Circuit Designs

1 code implementation • 6 Jan 2020 • Keertana Settaluri, Ameer Haj-Ali, Qijing Huang, Kourosh Hakhamaneshi, Borivoje Nikolic

Domain specialization under energy constraints in deeply-scaled CMOS has been driving the need for agile development of Systems on a Chip (SoCs).

Signal Processing

Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration

5 code implementations • 22 Nov 2019 • Hasan Genc, Seah Kim, Alon Amid, Ameer Haj-Ali, Vighnesh Iyer, Pranav Prakash, Jerry Zhao, Daniel Grubb, Harrison Liew, Howard Mao, Albert Ou, Colin Schmidt, Samuel Steffl, John Wright, Ion Stoica, Jonathan Ragan-Kelley, Krste Asanovic, Borivoje Nikolic, Yakun Sophia Shao

DNN accelerators are often developed and evaluated in isolation without considering the cross-stack, system-level effects in real-world environments.

FireSim: FPGA-Accelerated Cycle-Exact Scale-Out System Simulation in the Public Cloud

1 code implementation • 45th ACM/IEEE International Symposium on Computer Architecture (ISCA 2018) 2018 • Sagar Karandikar, Howard Mao, Donggyu Kim, David Biancolin, Alon Amid, Dayeol Lee, Nathan Pemberton, Emmanuel Amaro, Colin Schmidt, Aditya Chopra, Qijing Huang, Kyle Kovacs, Borivoje Nikolic, Randy Katz, Jonathan Bachrach, Krste Asanovic

We present FireSim, an open-source simulation platform that enables cycle-exact microarchitectural simulation of large scale-out clusters by combining FPGA-accelerated simulation of silicon-proven RTL designs with a scalable, distributed network simulation.