The world's appetite for analyzing massive amounts of structured and unstructured data has grown dramatically. The computational demands of these abundant-data applications, such as deep learning, far exceed the capabilities of today's computing systems and are unlikely to be met with isolated improvements in transistor or memory technologies, or integrated circuit architectures alone. To achieve unprecedented functionality, speed, and energy efficiency, one must create transformative nanosystems whose architectures are based on the salient properties of the underlying nanotechnologies. Our Nano-Engineered Computing Systems Technology (N3XT) approach makes such nanosystems possible through new computing system architectures leveraging emerging device (logic and memory) nanotechnologies and their dense 3-D integration with fine-grained connectivity to immerse computing in memory and new logic devices (such as carbon nanotube field-effect transistors for implementing high-speed and low-energy logic circuits) as well as high-density nonvolatile memory (such as resistive memory), and amenable to ultradense (monolithic) 3-D integration of thin layers of logic and memory devices that are fabricated at low temperature. In addition, we explore the use of several device and integration technologies in the N3XT beyond the specific ones mentioned earlier that are also used in our main nanosystem prototypes. We also present an efficient resiliency technique to overcome endurance challenges in certain resistive memory technologies. N3XT hardware prototypes demonstrate the practicality of our architectures. We evaluate the benefits of the N3XT using a simulation framework calibrated using experimental measurements. System-level energy-delay product of common implementations of abundant-data workloads improves by three orders of magnitude in the N3XT compared with conventional architectures. These improvements impact a broad range of application workloads and architecture configurations, from embedded systems to the cloud.

We demonstrate a spiking neural circuit for azimuth angle detection inspired by the echolocation circuits of the Horseshoe bat Rhinolophus ferrumequinum and utilize it to devise a model for navigation and target tracking, capturing several key aspects of information transmission in biology. Our network, using only a simple local-information based sensor implementing the cardioid angular gain function, operates at biological spike rate of 10 Hz. The network tracks large angular targets (60 degrees) within 1 sec with a 10% RMS error. We study the navigational ability of our model for foraging and target localization tasks in a forest of obstacles and show that our network requires less than 200X spike-triggered decisions, while suffering only a 1% loss in performance compared to a proportional-integral-derivative controller, in the presence of 50% additive noise. Superior performance can be obtained at a higher average spike rate of 100 Hz and 1000 Hz, but even the accelerated networks requires 20X and 10X lesser decisions respectively, demonstrating the superior computational efficiency of bio-inspired information processing systems.