An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks

Edge TPUs are a domain of accelerators for low-power, edge devices and are widely used in various Google products such as Coral and Pixel devices. In this paper, we first discuss the major microarchitectural details of Edge TPUs. Then, we extensively evaluate three classes of Edge TPUs, covering different computing ecosystems, that are either currently deployed in Google products or are the product pipeline, across 423K unique convolutional neural networks. Building upon this extensive study, we discuss critical and interpretable microarchitectural insights about the studied classes of Edge TPUs. Mainly, we discuss how Edge TPU accelerators perform across convolutional neural networks with different structures. Finally, we present our ongoing efforts in developing high-accuracy learned machine learning models to estimate the major performance metrics of accelerators such as latency and energy consumption. These learned models enable significantly faster (in the order of milliseconds) evaluations of accelerators as an alternative to time-consuming cycle-accurate simulators and establish an exciting opportunity for rapid hard-ware/software co-design.Comment: 11 pages, 15 figures, submitted to ISCA 202

Reducing the Dimension of Online Calibration in Dynamic Traffic Assignment Systems

Effective real-time traffic management strategies often require dynamic traffic assignment systems that are calibrated online. But the computationally intensive nature of online calibration limits their application to smaller networks. This paper presents a dimensionality reduction of the online calibration problem that is based on principal components to overcome this limitation. To demonstrate this approach, the origin–destination flow estimation problem is formulated in relation to its principal components. The efficacy of the procedure was tested with real data on the Singapore Expressway network in an open-loop framework. A reduction in the problem dimension by a factor of 50 was observed with only a 2% loss in estimation accuracy. Further, the computational times were reduced by an order of 100. The procedure led to better predictions, as the principal components captured the structural spatial relationships. This work has the potential to make the online calibration problem more scalable

Improved Calibration Method for Dynamic Traffic Assignment Models

The calibration of dynamic traffic assignment (DTA) models involves the estimation of model parameters to best replicate real-world measurements. Good calibration is essential to estimate and predict accurately traffic states, which are crucial for traffic management applications to alleviate congestion. A widely used approach to calibrate simulation-based DTA models is the extended Kalman filter (EKF). The EKF assumes that the DTA model parameters are unconstrained, although they are in fact constrained; for instance, origin–destination (O-D) flows are nonnegative. This assumption is typically not problematic for small- and medium-scale networks in which the EKF has been successfully applied. However, in large-scale networks (which typically contain numbers of O-D pairs with small magnitudes of flow), the estimates may severely violate constraints. In consequence, simply truncating the infeasible estimates may result in the divergence of EKF, leading to extremely poor state estimations and predictions. To address this issue, a constrained EKF (CEKF) approach is presented; it imposes constraints on the posterior distribution of the state estimators to obtain the maximum a posteriori (MAP) estimates that are feasible. The MAP estimates are obtained with a heuristic followed by the coordinate descent method. The procedure determines the optimum and are computationally faster by 31.5% over coordinate descent and by 94.9% over the interior point method. Experiments on the Singapore expressway network indicated that the CEKF significantly improved model accuracy and outperformed the traditional EKF (up to 78.17%) and generalized least squares (up to 17.13%) approaches in state estimation and prediction

A simulation-based evaluation of a Cargo-Hitching service for E-commerce using mobility-on-demand vehicles

Time-sensitive parcel deliveries, shipments requested for delivery in a day or less, are an increasingly important research subject. It is challenging to deal with these deliveries from a carrier perspective since it entails additional planning constraints, preventing an efficient consolidation of deliveries which is possible when demand is well known in advance. Furthermore, such time-sensitive deliveries are requested to a wider spatial scope than retail centers, including homes and offices. Therefore, an increase in such deliveries is considered to exacerbate negative externalities such as congestion and emissions. One of the solutions is to leverage spare capacity in passenger transport modes. This concept is often denominated as cargo-hitching. While there are various possible system designs, it is crucial that such solution does not deteriorate the quality of service of passenger trips. This research aims to evaluate the use of Mobility-On-Demand services to perform same-day parcel deliveries. For this purpose, we use SimMobility, a high-resolution agent-based simulation platform of passenger and freight flows, applied in Singapore. E-commerce demand carrier data are used to characterize simulated parcel delivery demand. Operational scenarios that aim to minimize the adverse effect of fulfilling deliveries with Mobility-On-Demand vehicles on Mobility-On-Demand passenger flows (fulfillment, wait and travel times) are explored. Results indicate that the Mobility-On-Demand services have potential to fulfill a considerable amount of parcel deliveries and decrease freight vehicle traffic and total vehicle-kilometers-travelled without compromising the quality of Mobility On-Demand for passenger travel.Comment: 19 pages, 4 tables, 7 figures. Submitted to Transportation (Springer

Effect of combination therapy with pramipexole and n-acetylcysteine on global cerebral ischemic reperfusion injury in rats

Objective(s): The study was intended to investigate the combined influence of two neuroprotective agents pramipexole and n-acetylcysteine on global cerebral ischemic reperfusion injury (GCIRI) model in rats.Materials and Methods: GCIRI was induced by bilateral common carotid artery ligation (BCCA) in rats. Animals were divided into six groups. Groups I, II, and III received saline intraperitoneally (IP) (5 ml/kg/day, 0.9 % saline). The remaining groups IV, V, and VI were treated with n-acetylcysteine (NAC-150 mg/kg/day, IP), pramipexole (PPX-0.23 mg/kg/day, IP) alone and in combination, respectively. BCCA was done in all groups except in groups I (control) and II (sham control) of animals. The treatment was given for one week before the surgery and continued for two days after surgery. Subsequently, behavioral performances, biochemical estimations, proinflammatory cytokines, and histopathological evaluations were done.Results: NAC, PPX, and combination treatment groups showed significant ameliorative effects on behavioral, biochemical, proinflammatory cytokines, and histopathological studies as compared with the BCCA group. Whereas, the combination group showed a significant difference in ameliorating the pathological changes of biochemical parameters and histopathological changes in comparison with the PPX alone treated group but not with the NAC alone group. Conclusion: The study concluded that in the combination treatment group the histopathological parameter improved and the oxidative stress parameters were mitigated significantly compared with the PPX alone treatment group but not with the NAC alone treatment group