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Even though probabilistic treatments of neural networks have a long history, they have not found widespread use in practice. Sampling approaches are often too slow already for simple networks. The size of the inputs and the depth of typical…

Augmentative and Alternative Communication (AAC) systems allow people with disabilities to provide input to devices which empower them to more fully interact with their environment. Within AAC, switch scanning is a common paradigm for…

We propose Impatient Deep Neural Networks (DNNs) which deal with dynamic time budgets during application. They allow for individual budgets given a priori for each test example and for anytime prediction, i.e., a possible interruption at…

Deep neural network (DNN) based approaches hold significant potential for reinforcement learning (RL) and have already shown remarkable gains over state-of-art methods in a number of applications. The effectiveness of DNN methods can be…

Designing lightweight convolutional neural network (CNN) models is an active research area in edge AI. Compute-in-memory (CIM) provides a new computing paradigm to alleviate time and energy consumption caused by data transfer in von Neumann…

Recurrent networks have achieved great success on various sequential tasks with the assistance of complex recurrent units, but suffer from severe computational inefficiency due to weak parallelization. One direction to alleviate this issue…

Advances in deep neural networks (DNNs) have significantly contributed to the development of real-time video processing applications. Efficient scheduling of DNN workloads in cloud-hosted inference systems is crucial to minimizing serving…

While the GPGPU paradigm is widely recognized as an effective approach to high performance computing, its adoption in low-latency, real-time systems is still in its early stages. Although GPUs typically show deterministic behaviour in terms…

Global optimization of access point (AP) assignment to user terminals requires efficient monitoring of user behavior, fast decision algorithms, efficient control signaling, and fast AP reassignment mechanisms. In this scenario, software…

Software-defined networking (SDN) paradigm, with the flexible and logically centralized control, enables dynamically minimizing the network energy consumption by redirecting paths of packets. However, the links and switches are designed to…

Kahn Process Networks (KPNs) are a deterministic Model of Computation (MoC) for distributed systems. KPNs supports non-blocking writes and blocking reads, with the consequent assumption of unbounded buffers between processes. Variants such…

Location-aware networks will enable new services and applications in fields such as autonomous driving, smart cities, and the Internet-of-Things. One promising solution for ubiquitous localization is network localization and navigation…

This paper presents a power distribution network (PDN) decoupling capacitor optimization application with three primary goals: reduction of solution times for large networks, development of flexible network scoring routines, and a…

Wireless connectivity for the Internet of Things (IoT) requires combining low latency and high reliability with power and cost constraints. On top of that, diverse applications and end-to-end services with very different performance…

Deep neural networks (DNNs) have been deployed in myriad machine learning applications. However, advances in their accuracy are often achieved with increasingly complex and deep network architectures. These large, deep models are often…

With the growing demand for Earth observation, it is important to provide reliable real-time remote sensing inference services to meet the low-latency requirements. The Space Computing Power Network (Space-CPN) offers a promising solution…

Distributed deep neural networks (DNNs) have become central to modern computer vision, yet their deployment on resource-constrained edge devices remains hindered by substantial parameter counts, computational demands, and the probability of…

Rapid urbanization burdens city infrastructure and creates the need for local governments to maximize the usage of resources to serve its citizens. Smart city projects aim to alleviate the urbanization problem by deploying a vast amount of…

Designing the power delivery network (PDN) in very large-scale integrated (VLSI) circuits is increasingly important, especially for nowadays low-power integrated circuit (IC) design. In order to ensure that the designed PDN enables a low…

Low-light image enhancement strives to improve the contrast, adjust the visibility, and restore the distortion in color and texture. Existing methods usually pay more attention to improving the visibility and contrast via increasing the…