ELoNC
Project: Exploring the Logic of Neural Computing
Collaborating departments: Electrical and Computer Engineering (TUM); Computer and Communication Sciences (EPFL)
From neural networks to circuits
Inspired by the human brain, neural networks attempt to emulate the way the brain works. The last decade has witnessed the breakthrough of neural networks in various fields, e.g., image/speech processing. Despite of the huge success of neural networks, their enormous power consumption executing on digital hardware, e.g., TPU and GPU, restricts their applications in resource-constrained platforms, e.g., edge devices. The large power consumption results from a huge number of MAC operations and data movement.
To address the power consumption problem, ELoNC, a joint project of TUM and EPFL targets to explore the relationship between neural networks and their circuit implementations. Specifically, the feasibility of directly converting neural networks into digital circuits will be verified. The team at TUM will focus on developing various training methods to enable the conversion of neural networks into circuits and enhance logic synthesis for such conversion. The team at EPFL will support the logic synthesis and verify the resulting circuits on FPGAs.
Team
Coordinating Postdoc
Dr.-Ing. Li (Grace) Zhang
Chair of Electronic Design Automation | TUM
Doctoral Candidate
Riudi Qui
Chair of Electronic Design Automation | TUM
Doctoral Candidate
Wenhao Sun
Chair of Electronic Design Automation | TUM
Principal Investigator
Prof. Dr.-Ing. Ulf Schlichtmann
Chair of Electronic Design Automation | TUM
Principal Investigator
Professor Paolo Ienne
Processor Architecture Laboratory | EPFL