5.1 SPECIAL DAY Hot Topic: Building Confidence in Advanced Driver Assistance Systems

Date: Wednesday 16 March 2016
Time: 08:30 - 10:00
Location / Room: Saal 2

Organisers:
Samarjit Chakraborty, Technische Universität München (TUM), DE
Wolfgang Ecker, Infineon Technologies, DE

Chair:
Sebastian Steinhorst, TUM CREATE, SG

Co-Chair:
Kai Lampka, Uppsala University, SE

With the recent evolutions of nanometer transistor technologies, power consumption emerged as the most critical limitation. Within advanced processors and computing architectures, the processor-memory communication accounts for a significant part of the energy requirement. While alternative design approaches, such as the use of optimized accelerators or advanced power management techniques are successfully employed in contemporary designs, the trend keeps worsening due to the ever-increasing gap between on-chip and off-chip memory data rates. This trend, known as Von Neumann bottleneck, not only limits the system performance, but also acts nowadays as a limiter of the energy scaling. The quest towards more energy efficiency requires solutions that solve the Von Neumann bottleneck by tightly intertwining computing with memories. In this hot topic session, we intend to elaborate on in-memory computing by identifying its current applications and its promises in light of emerging technologies. In-memory computing is considered here in the general sense of computing information locally within large data storage. Four talks will be provided. The first talk will cover the current industrial applications of in-memory computing to achieve energy efficient acceleration. The three other talks will explore the opportunities of in-memory systems realized with emerging technologies. In particular, we will see how the memristor theory can benefit to Cellular Neural Network (CNN). We will also dig into the recently introduced concept of memcomputing that promises to speed up the execution of NP-complete problems. Finally, we will present a novel computer architecture that relies on resistive memory elements to compute and store information.