As the semiconductor industry struggles to keep Moore's law alive and integrate more functionality on a chip, multi-chiplet chips offer a lower cost alternative to large monolithic chips due to their higher yield....
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ISBN:
(数字)9783982674100
ISBN:
(纸本)9798331534646
As the semiconductor industry struggles to keep Moore's law alive and integrate more functionality on a chip, multi-chiplet chips offer a lower cost alternative to large monolithic chips due to their higher yield. However, chiplet-based chips are naturally Non-Uniform Memory Access (NUMA) systems and therefore suffer from slow remote accesses. NUMA overheads are exacerbated by the limited throughput and higher latency of inter-chiplet communication. This paper offers a comprehensive analysis of chiplet-based systems with different design parameters measuring their performance overheads compared to traditional monolithic multicore designs and their scalability to system and chiplet size. Several design alternatives pertaining to the memory hierarchy, interconnects, and technology aspects are studied. Our analysis shows that although chiplet-based chips can cut (recurring engineering) costs to half, they may give away over a third of the monolithic performance. Part of this performance overhead can be regained with specific design choices.
We investigate the behavior of methods that use linear projections to remove information about a concept from a language representation, and we consider the question of what happens to a dataset transformed by such a ...
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We derived kinetic sources of energetic electrons generated by tritium beta decay and Compton scattering and compared them to the corresponding fluid RE generation rates [2]. In the SPARC example considered, using kin...
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We evaluate a battery of recent large language models on two benchmarks for word sense disambiguation in Swedish. At present, all current models are less accurate than the best supervised disambiguators in cases where...
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For the telephone broadcast model, an O(n log n)-time algorithm for constructing an optimal broadcasting scheme in a star of cliques with a total of n vertices was recently presented by Ambashankar and Harutyunyan at ...
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Stream Aggregates are crucial in digital infrastructures for transforming continuous data streams into actionable insights. However, state-of-the-art Stream Processing Engines lack mechanisms to effectively balance pe...
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ISBN:
(纸本)9798400710735
Stream Aggregates are crucial in digital infrastructures for transforming continuous data streams into actionable insights. However, state-of-the-art Stream Processing Engines lack mechanisms to effectively balance performance with memory consumption - a capability that is especially crucial in environments with fluctuating computational resources and data-intensive *** paper tackles this gap by introducing a novel on-demand adaptive memory compression scheme for stream Aggregates. Our approach uses Reinforcement Learning (RL) to dynamically adapt how a stream Aggregate compresses its state, balancing performance and memory utilization under a given processing latency threshold. We develop a model that incorporates the application- and data-specific nuances of stream Aggregates and create a framework to train RL Agents to adjust memory compression levels in real-time. Additionally, we shed light on a trade-off between the timeliness of an RL Agent training and its resulting behavior, defining several policies to account for this *** extensive evaluation, we show that the proposed RL Agent supports well on-demand memory compression. We also study the effects of our policies - providing guidance on their role in RL applied to stream Aggregates - and show our framework supports lean execution of such RL jobs.
Data is key for rapid and continuous delivery of customer value. By collecting data from products in the field, companies in the embedded systems domain can measure and monitor product performance and they get the opp...
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ISBN:
(数字)9798350380262
ISBN:
(纸本)9798350380279
Data is key for rapid and continuous delivery of customer value. By collecting data from products in the field, companies in the embedded systems domain can measure and monitor product performance and they get the opportunity to provide customers with insights and data-driven services. However, while the notion of data-driven development is not new, embedded systems companies are facing a situation in which data volumes are growing exponentially and this is not without its challenges. Suddenly, the cost of collecting, storing and processing data becomes a concern and while there is prominent research on different aspects of data-driven development, there is little guidance for how to reason about business value versus costs of data. In this paper, we present findings from case study research conducted in close collaboration with four companies in the embedded systems domain. The contribution of this paper is a framework that provides a holistic understanding of the multiple dimensions that need to be considered when reasoning about business value versus cost of collecting, storing and processing data.
Cyber-physical systems are often safety-critical and their correctness is crucial, as in the case of automated driving. Using formal mathematical methods is one way to guarantee correctness. Though these methods have ...
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Interest in sustainability research has increased in Software engineering, however most research conducted over-look startups integrating sustainability into the development of their software systems. The goal of this...
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This study explores an innovative Artificial Intelligence (AI) - based solution for enhancing material procurement and cost estimation in engineering, Procurement and Construction (EPC) projects, particularly within t...
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