Researcher: Unai Alegre-Ibarra
Autonomous and computer-assisted driving are disruptively changing the automotive industry. The idea is to have vehicles with little or no human input, which are able to sense their environment, and safely navigate through it. Additionally, advanced driver assistance systems (ADAS), also provide support for the driver while parking or driving. The driver can receive assistance which is partially, highly or fully automated.
In order to perceive their surrounding environment, vehicles are equipped with vision (cameras, LiDAR, radar), sound (microphones) and location (GPS/IMU) sensors . The vehicle needs to position itself, not only on the map, but also needs to have some scene understanding, as well as a driving policy, to plan actions according to the different situations that might occur. Autonomous driving needs to actively recognize and classify relevant traffic events. For instance, the vehicle might require to understand and process traffic signs and lanes, as well as surrounding objects/people/obstacles, their expected behavior when next to them (such as yielding to an ambulance, or driving more carefully when the pedestrians are children), as well as their expected behavior around the autonomous vehicle (if they are going to stay still, move, yield, etc.) . In addition, they need to process all this information while making the right driving decision in real-time, which requires considerable on-board intelligence.
Artificial intelligence (AI) is widely considered crucial for optimally implementing this technology. Automotive giants such as Tesla and Google are spending millions of dollars on research to make autonomous cars a commercial reality. Recently, Ford Motor Company made an investment of one billion dollars in Argo AI, a new AI company to bring forth a virtual driver system in the future, possibly by 2021 . A successful branch of Artificial Intelligence is that of neural-networks, which have proven to be successful in surpassing human performance on different specific tasks, such as playing the GO game . The success of neural-networks, and in particular that of deep neural networks (DNNs), comes with an exponential increase in the number of parameters and operations, which brings along high energy costs, high latency, and massive hardware infrastructure. This computational demand coincides with the slowdown of Moore’s law, highlighting the need to search for other computational solutions beyond cramming more transistors in the same circuit area. There is a broad spectrum of research on hardware acceleration solutions for obtaining state of the art performance in DNNs while reducing associated costs.
The aim of the project NANO(AI)2 is to develop low-power, reconfigurable, nano-electronic devices in such a way that they can be applicable for reducing computational costs in tasks related to autonomous driving, in the automotive industry. Our systems consist of bottom-up assembled nano-scale material networks integrated with conventional top-down CMOS electronics. The project is a close collaboration between physicists, electrical engineers and computer scientists at BRAINS and the four users, and is based on the recent breakthrough results obtained . We use the concept of a Dopant Network Processing Unit (DNPU) , a lightly doped (n- or p-type) semiconductor with a nano-scale active region contacted by several electrodes. Different materials can be used as dopant or host, and the number of electrodes can vary. Some of the electrodes, called activation electrodes, receive a voltage input, producing a current on a selected output electrode. The dopants in the active region form an atomic-scale network through which the electrons can hop from one electrode to another. This physical process results in an output current at the readout which depends non-linearly on the voltages applied at the activation electrodes. By tuning the voltages applied to some of the electrodes, the output current can be controlled as a function of the voltages at the remaining electrodes. This tunability can be exploited to solve various linearly non-separable classification tasks, which are equivalent to tasks that a small artificial neural network can solve.
We are currently exploring the scalability of DNPUs, and its applicability to different tasks related to autonomous driving:
- Static task and static feature: Classifying a static object like a road sign while standing still, e.g., at a traffic light
- Dynamic task and static feature: Classifying a static object while driving
- Static task and dynamic feature: Classifying a moving object while standing still
- Dynamic task and dynamic feature Classifying a moving object while driving
 Lex Friedman, 6.S094: Deep Learning for Self-Driving Cars," https://selfdrivingcars.mit.edu/, 2018, [Online; accessed 7-August-2020].
 Hodges, C., An, S., Rahmani, H., & Bennamoun, M. (2019). Deep Learning for Driverless Vehicles. In Handbook of Deep Learning Applications (pp. 83-99). Springer, Cham.
 S.K. Bose, C.P. Lawrence, Z. Liu, K.S. Makarenko, R.M.J. van Damme, H.J. Broersma and W.G. van der Wiel, Evolution of a Designless Nanoparticle Network into Reconfigurable Boolean Logic, Nature Nanotechnology 10, 1048 (2015).
 Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., ... & Dieleman, S. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484-489.
 Chen, T., van Gelder, J., van de Ven, B., Amitonov, S. V., de Wilde, B., Euler, H. C. R., ... & van der Wiel, W. G. (2020). Classification with a disordered dopant-atom network in silicon. Nature, 577(7790), 341-345.
 Euler, H. C. R., Alegre-Ibarra, U., van de Ven, B., Broersma, H., Bobbert, P. A., & van der Wiel, W. G. (2020). Dopant Network Processing Units: Towards Efficient Neural-network Emulators with High-capacity Nanoelectronic Nodes. arXiv preprint arXiv:2007.12371.