97 research outputs found
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
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
Improved indexing for cache miss reduction in embedded systems
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 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, in devices where the application set is known and characterized (e.g., systems that execute a fixed application set) 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 give an efficient heuristic algorithm for selecting K index bits for improved cache performance. We show the feasibility of our algorithm by applying it to a large number of embedded system applications as well as the integer SPEC CPU 2000 benchmarks
Analytical design space exploration of caches for embedded systems
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. Traditionally, a design-simulate-analyze methodology is used to achieve desired cache performance. Here, to bootstrap the process, arbitrary cache parameters are selected, the cache sub-system is simulated using a cache simulator, based on performance results, cache parameters are tuned, and the process is repeated until an acceptable design is obtained. Since the cache design space is typically very large, the traditional approach often requires a very long time to converge. In the proposed approach, we outline an efficient algorithm that directly computes cache parameters satisfying the desired performance. We demonstrate the feasibility of our algorithm by applying it to a large number of embedded system benchmarks
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