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Coarse-grained reconfigurable architectures aim to achieve both goals of high performance and flexibility. However, existing reconfigurable array architectures require many resources without considering the specific application domain.…
Recently, the growing memory demands of embedding tables in Deep Learning Recommendation Models (DLRMs) pose great challenges for model training and deployment. Existing embedding compression solutions cannot simultaneously meet three key…
We develop a tool called PipeGen for efficient data transfer between database management systems (DBMSs). PipeGen targets data analytics workloads on shared-nothing engines. It supports scenarios where users seek to perform different parts…
Parallel dataflow systems are a central part of most analytic pipelines for big data. The iterative nature of many analysis and machine learning algorithms, however, is still a challenge for current systems. While certain types of bulk…
Modern distributed data processing systems struggle to balance performance, maintainability, and developer productivity when integrating machine learning at scale. These challenges intensify in large collaborative environments due to high…
Machine learning models are often implemented in cohort with humans in the pipeline, with the model having an option to defer to a domain expert in cases where it has low confidence in its inference. Our goal is to design mechanisms for…
Rapid progress in deep learning is leading to a diverse set of quickly changing models, with a dramatically growing demand for compute. However, as frameworks specialize performance optimization to patterns in popular networks, they…
Owing to their cost-effectiveness and flexibility, cloud services have been the default choice for the deployment of innumerable software systems over the years. However, novel paradigms are beginning to emerge, as the cloud can't meet the…
The emergence of the Internet of Things has seen the introduction of numerous connected devices used for the monitoring and control of even Critical Infrastructures. Distributed stream processing has become key to analyzing data generated…
The CODECO Experimentation Framework is an open-source solution designed for the rapid experimentation of Kubernetes-based edge cloud deployments. It adopts a microservice-based architecture and introduces innovative abstractions for (i)…
With the rise of the fine-tuned-pretrained paradigm, storing numerous fine-tuned models for multi-tasking creates significant storage overhead. Delta compression alleviates this by storing only the pretrained model and the highly compressed…
Real-time multimodal inference on resource-constrained edge devices is essential for applications such as autonomous driving, human-computer interaction, and mobile health. However, prior work often overlooks the tight coupling between…
The pivotal key to the success of manufacturing enterprises is a sustainable and innovative product design and development. In collaborative design, stakeholders are heterogeneously distributed chain-like. Due to the growing volume of data…
In this paper, we summarize our effort to create and utilize a simple framework to coordinate computational analytics tasks with the help of a workflow system. Our design is based on a minimalistic approach while at the same time allowing…
Modern machine learning models have started to consume incredible amounts of energy, thus incurring large carbon footprints (Strubell et al., 2019). To address this issue, we have created an energy estimation pipeline1, which allows…
As organizations increasingly rely on data-driven insights, the ability to run data intensive applications seamlessly across multiple cloud environments becomes critical for tapping into cloud innovations while complying with various…
In recent years, the integration of artificial intelligence (AI) and cloud computing has emerged as a promising avenue for addressing the growing computational demands of AI applications. This paper presents a comprehensive study of…
Building environments are significant sources of global energy consumption. To create energy efficient buildings, the first step is to sense and monitor all the energy-consuming appliances in the buildings and record all the energy…
Optimization pipelines targeting polyhedral programs try to maximize the compute throughput. Traditional approaches favor reuse and temporal locality; while the communicated volume can be low, failure to optimize spatial locality may cause…
This paper attends to the problem of embedding flexibly specified CloudNets, virtual networks connecting cloud resources (such as storage or computation). We attend to a scenario where customers can request CloudNets at short notice, and an…