Fine-grained acceleration control for autonomous intersection management using deep reinforcement learning

Recent advances in combining deep learning and Reinforcement Learning have shown a promising path for designing new control agents that can learn optimal policies for challenging control tasks. These new methods address the main limitations of conventional Reinforcement Learning methods such as customized feature engineering and small action/state space dimension requirements. In this paper, we leverage one of the state-of-the-art Reinforcement Learning methods, known as Trust Region Policy Optimization, to tackle intersection management for autonomous vehicles. We show that using this method, we can perform fine-grained acceleration control of autonomous vehicles in a grid street plan to achieve a global design objective.Comment: Accepted in IEEE Smart World Congress 201

OPEB: Open Physical Environment Benchmark for Artificial Intelligence

Artificial Intelligence methods to solve continuous- control tasks have made significant progress in recent years. However, these algorithms have important limitations and still need significant improvement to be used in industry and real- world applications. This means that this area is still in an active research phase. To involve a large number of research groups, standard benchmarks are needed to evaluate and compare proposed algorithms. In this paper, we propose a physical environment benchmark framework to facilitate collaborative research in this area by enabling different research groups to integrate their designed benchmarks in a unified cloud-based repository and also share their actual implemented benchmarks via the cloud. We demonstrate the proposed framework using an actual implementation of the classical mountain-car example and present the results obtained using a Reinforcement Learning algorithm.Comment: Accepted in 3rd IEEE International Forum on Research and Technologies for Society and Industry 201

Always-Sparse Training by Growing Connections with Guided Stochastic Exploration

The excessive computational requirements of modern artificial neural networks (ANNs) are posing limitations on the machines that can run them. Sparsification of ANNs is often motivated by time, memory and energy savings only during model inference, yielding no benefits during training. A growing body of work is now focusing on providing the benefits of model sparsification also during training. While these methods greatly improve the training efficiency, the training algorithms yielding the most accurate models still materialize the dense weights, or compute dense gradients during training. We propose an efficient, always-sparse training algorithm with excellent scaling to larger and sparser models, supported by its linear time complexity with respect to the model width during training and inference. Moreover, our guided stochastic exploration algorithm improves over the accuracy of previous sparse training methods. We evaluate our method on CIFAR-10/100 and ImageNet using ResNet, VGG, and ViT models, and compare it against a range of sparsification methods

Convolution and Cross-Correlation of Count Sketches Enables Fast Cardinality Estimation of Multi-Join Queries

With the increasing rate of data generated by critical systems, estimating functions on streaming data has become essential. This demand has driven numerous advancements in algorithms designed to efficiently query and analyze one or more data streams while operating under memory constraints. The primary challenge arises from the rapid influx of new items, requiring algorithms that enable efficient incremental processing of streams in order to keep up. A prominent algorithm in this domain is the AMS sketch. Originally developed to estimate the second frequency moment of a data stream, it can also estimate the cardinality of the equi-join between two relations. Since then, two important advancements are the Count sketch, a method which significantly improves upon the sketch update time, and secondly, an extension of the AMS sketch to accommodate multi-join queries. However, combining the strengths of these methods to maintain sketches for multi-join queries while ensuring fast update times is a non-trivial task, and has remained an open problem for decades as highlighted in the existing literature. In this work, we successfully address this problem by introducing a novel sketching method which has fast updates, even for sketches capable of accurately estimating the cardinality of complex multi-join queries. We prove that our estimator is unbiased and has the same error guarantees as the AMS-based method. Our experimental results confirm the significant improvement in update time complexity, resulting in orders of magnitude faster estimates, with equal or better estimation accuracy.Comment: Accepted at the International Conference on Management of Data 202

Adaptive resource synchronization in hierarchical real-time systems

In this paper we outline the Adaptive Resource Allocation Protocol (ARAP) as an improved resource synchronization algorithm for hierarchically scheduled real-time systems. ARAP exploits knowledge about task utilization, using a proportional-integral-derivative (PID) controller, to estimate required resource bandwidth and improve scheduling decisions. Our analysis and experiments with RTSIM show that ARAP provides better temporal isolation and resource utilization during periods of transient overload compared to state-of-the-art resource synchronization algorithms. Implemented as part of VxWorks , the results are confirmed using an avionic system, for which ARAP substantially reduced the number of hard real-time deadline misses. </jats:p

DotHash: Estimating Set Similarity Metrics for Link Prediction and Document Deduplication

Metrics for set similarity are a core aspect of several data mining tasks. To remove duplicate results in a Web search, for example, a common approach looks at the Jaccard index between all pairs of pages. In social network analysis, a much-celebrated metric is the Adamic-Adar index, widely used to compare node neighborhood sets in the important problem of predicting links. However, with the increasing amount of data to be processed, calculating the exact similarity between all pairs can be intractable. The challenge of working at this scale has motivated research into efficient estimators for set similarity metrics. The two most popular estimators, MinHash and SimHash, are indeed used in applications such as document deduplication and recommender systems where large volumes of data need to be processed. Given the importance of these tasks, the demand for advancing estimators is evident. We propose DotHash, an unbiased estimator for the intersection size of two sets. DotHash can be used to estimate the Jaccard index and, to the best of our knowledge, is the first method that can also estimate the Adamic-Adar index and a family of related metrics. We formally define this family of metrics, provide theoretical bounds on the probability of estimate errors, and analyze its empirical performance. Our experimental results indicate that DotHash is more accurate than the other estimators in link prediction and detecting duplicate documents with the same complexity and similar comparison time

Optimal Cache Organization using an Allocation Tree

The increasing use of microprocessor cores in embedded systems, as well as mobile and portable devices, creates an opportunity for customizing the cache subsystem for improved performance. In this work, we outline a technique based on a binary tree data structure to efficiently compute a set of cache size and associativity pairs that result in ideal cache behavior for a given application memory reference trace. In ideal caches, the number of cache misses is reduced to only cold misses while conflict and capacity misses are avoided entirely. Idea cache behavior is critical in real time applications, where it is desired to accurately estimate the worse case execution time of a task in the presence of caches. Likewise, ideal caches play an important role in low power applications by reducing data transmission to/from off-chip memory to a minimum. We demonstrate the feasibility of our algorithm by applying it to a large number of embedded system benchmarks

Optimal Indexing for Cache Miss Reduction in Embedded Systems

The increasing use of microprocessor cores in embedded systems creates an opportunity for customizing the cache subsystem for improved performance. In traditional cache design, the index portion of the memory address bus consists of the K least significant bits, where K=log2(D) and D is the depth of the cache. However, for embedded systems that execute a fixed application, there is an opportunity to improve cache performance by choosing an optimal set of bits used as index into the cache. This technique does not add any overhead in terms of area or delay. We show that this problem belongs to the NP-complete class of problems. Further, we give a heuristic algorithm for selecting the K index bits that is efficient and produces good results. We show the feasibility of our algorithm by applying it to a large number of embedded system applications