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Resistive random access Memory (RRAM) based spiking neural networks (SNN) are becoming increasingly attractive for pervasive energy-efficient classification tasks. However, such networks suffer from degradation of performance (as determined by classification accuracy) due to the effects of process variations on fabricated RRAM devices resulting in loss of manufacturing yield. To address such yield loss, a two-step approach is developed. First, an alternative test framework is used to predict the performance of fabricated RRAM based SNNs using the SNN response to a small subset of images from the test image dataset, called the SNN response signature (to minimize test cost). This diagnoses those SNNs that need to be performance-tuned for yield recovery. Next, SNN tuning is performed by modulating the spiking thresholds of the SNN neurons on a layer-by-layer basis using a trained regressor that maps the SNN response signature to the optimal spiking thresholdvalues during tuning. The optimal spiking threshold values are determined by an off-line optimization algorithm. Experiments show that the proposed framework can reduce the number of out-of-spec SNN devices by up to 54% and improve yield by as much as 8.6%. 

Not AvailableThis paper presents a Light Detection and Ranging (LiDAR) based technique for identifying non-line-of-sight (NLoS) communication paths in indoor millimeter-wave (mmWave) environments. This is achieved by using LiDAR distance and backscattered intensity measurements to i) create a map of the communication environment, and ii) identify a set of potential reflective surfaces for NLoS communication. Experimental results demonstrate that the proposed technique, which combines environmental geometry and LiDAR scatter data for ray-tracing, achieves performance comparable to traditional exhaustive search beam training methods, particularly for first-ranked (top- 1) alternative NLoS paths. This offers a promising solution for enhancing indoor mmWave communications in dynamic environments prone to line-of-sight link blockages without the need for a fully dedicated beam training stage. 

Not AvailableIEEE 

IEEE 

This paper presents a reactive planning system that allows a Cassie-series bipedal robot to avoid multiple non-overlapping obstacles via a single, continuously differentiable control barrier function (CBF). The overall system detects an individual obstacle via a height map derived from a LiDAR point cloud and computes an elliptical outer approximation, which is then turned into a CBF. The QP-CLF-CBF formalism developed by Ames et al. is applied to ensure that safe trajectories are generated. Safe planning in environments with multiple obstacles is demonstrated both in simulation and experimentally on the Cassie biped. 

In this paper we present an approximate division scheme for Scaled Population (SP) arithmetic, a technique that improves on the limitations of stochastic computing (SC). SP arithmetic circuits are designed (a) to perform all operations with a constant delay, and (b) they use scaling operations to help reduce errors compared to SC circuits. As part of this work, we also present a method to correlate two SP numbers with a constant delay. We compare our SP divider with SC dividers, as well as fixed-point dividers (in terms of area, power and delay). Our 512-bit SP divider has a delay (power) that is 0.08× (0.06×) that of the equivalent fixed-point binary divider. Compared to a equivalent SC divider, our power-delay-product is 13× better. Index Terms—Approximate Arithmetic, Stochastic Computing, Computer Arithmetic, Approximate Division, Fast Division