ReRAM has emerged as a promising candidate for computing-in-memory due to its excellent compatibility with advanced CMOS processes, high density, and non-volatility. Meanwhile, Digital Computing-in-Memory (DCIM) offers higher energy efficiency than its analog counterpart and supports full-precision processing. However, existing ReRAM-based DCIMs are hindered by error-prone readout and low input parallelism. The inefficient bit-width reconfiguration also incurs significant hardware overhead during neural network mapping. This paper reports a 64Kb DCIM engine based on 1T2R ReRAM bitcells for multiply-and-Accumulate(MAC) acceleration. The ternary-input Booth-assisted multiply-in-memory (TB-MIM) flow allows bi-state ReRAM with limited readout margin to achieve high-accuracy computation with high parallelism. The corresponding reconfigurable vector adder-tree and accumulator (ReV-A2) effectively facilitate practical hybrid-precision network mapping. Simulations show that the proposed DCIM engine achieves a normalized throughput rate of 284.44 GOPS/Kb and an energy efficiency of 730.16 TOPS/W, surpassing previous ReRAM-based DCIM designs by$3.3\times $.

We address the problem of efficient 3-D exploration in indoor environments for micro aerial vehicles with limited sensing capabilities and payload/power constraints. We develop an indoor exploration framework that uses learning to predict the occupancy of unseen areas, extracts semantic features, samples viewpoints to predict information gains for different exploration goals, and plans informative trajectories to enable safe and smart exploration. Extensive experimentation in simulated and real-world environments shows the proposed approach outperforms the state-of-the-art exploration framework by 24% in terms of the total path length in a structured indoor environment and with a higher success rate during exploration.

In this work we propose long wave infrared (LWIR) imagery as a viable supporting modality for semantic segmentation using learning-based techniques. We first address the problem of RGB-thermal camera calibration by proposing a passive calibration target and procedure that is both portable and easy to use. Second, we present PST900, a dataset of 894 synchronized and calibrated RGB and Thermal image pairs with per pixel human annotations across four distinct classes from the DARPA Subterranean Challenge. Lastly, we propose a CNN architecture for fast semantic segmentation that combines both RGB and Thermal imagery in a way that leverages RGB imagery independently. We compare our method against the state-of-the-art and show that our method outperforms them in our dataset.

This study examines driver engagement with smartwatches and smartphones while driving. Twelve participants (7 novice and 5 experienced smartwatch users) drove in a high-fidelity simulator while receiving notifications from either a smartwatch (Pebble) or a smartphone (LG Nexus 5). It was found that participants had more glances, on average, per notification while using the smartwatch compared to the smartphone. Further, their brake response times were longer when they received notifications prior to a lead vehicle braking event on the smartwatch compared to when they did not receive any notifications and when they received notifications on the smartphone. Contrary to these glance and driving performance findings, participants perceived similar levels of risk for the two devices, and they largely reported that smartwatch use while driving should receive penalties equal to or less than smartphone use with respect to distracted driving legislation. Thus, there appears to be a disconnection between drivers' actual performance while using smartwatches and their perceptions.