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Bayesian inference is a probabilistic approach to the problem of drawing conclusions from observed data. Its main challenge is computational, which the Bayesian community tends to address through approximation techniques. However, these techniques come ...

Spiking Neural Networks (SNNs) represent a promising solution for streaming applications at the edge that have strict performance and energy requirements. However, implementing SNNs efficiently at the edge requires model quantization to reduce ...

Memristor-based in-memory neuromorphic computing systems promise a highly efficient implementation of vector-matrix multiplications, commonly used in artificial neural networks (ANNs). However, the immature fabrication process of memristors and circuit ...

Hybrid storage systems (HSS) use multiple different storage devices to provide high and scalable storage capacity at high performance. Data placement across different devices is critical to maximize the benefits of such a hybrid system. Recent research ...

Ongoing climate change calls for fast and accurate weather and climate modeling. However, when solving large-scale weather prediction simulations, state-of-the-art CPU and GPU implementations suffer from limited performance and high energy consumption. ...

We provide a set of generic modifications to improve the execution efficiency of single-shot object detectors by exploiting prior object locations in video sequences. We propose a crop-based method to accelerate object detection tasks. It ...$\mathrm{}\mathrm{}$$\mathrm{}\mathrm{}$$\mathrm{}\mathrm{}$

<u>C</u>omputation-<u>i</u>n-<u>m</u>emory (CIM) is one of the most appealing computing paradigms, especially for implementing artificial neural networks. Non-volatile memories like ReRAMs, PCMs, etc., have proven to be promising candidates for the ...

Neural pruning is a widely-used compression technique for Deep Neural Networks (DNNs). Recent innovations in Hardware Architectures (e.g. Nvidia Ampere Sparse Tensor Core) and N:M fine-grained Sparse Neural Network algorithms (i.e. every M-weights ...

Welcome to the SCOPES workshop. Due to the COVID-19 pandemic, we are meeting in a virtual venue instead of in Eindhoven. This year we are presenting a workshop program that features many interesting talks on all aspects related to the design of modern ...

Machine-learning-based models have recently gained traction as a way to overcome the slow downstream implementation process of FPGAs by building models that provide fast and accurate performance predictions. However, these models suffer from two main ...

Unmanned Aerial Vehicles (UAVs) that interact with the physical world in real-time make use of a multitude of sensors and often execute deep neural network workloads for perceiving the state of the environment. To increase UAV’s operations, it is ...

This article presents a modular automaton-based framework to specify flexible manufacturing systems and to optimize the makespan of product batches. The Batch Makespan Optimization (BMO) problem is NP-Hard and optimization can therefore take ...

The Halide DSL and compiler have enabled high-performance code generation for image processing pipelines targeting heterogeneous architectures through the separation of algorithmic description and optimization schedule. However, automatic schedule ...

Processors based on the von Neumann architecture show inefficient performance on many emerging data-intensive workloads. Computation in-memory (CIM) tries to address this challenge by performing the computation on the data location. To realize CIM,...

Development of hearing aid (HA) signal processing algorithms entails an iterative process between two design steps, namely algorithm development and the embedded implementation. Algorithm designers favor high-level programming languages for several ...

Deep learning models have replaced conventional methods for machine learning tasks. Efficient inference on edge devices with limited resources is key for broader deployment. In this work, we focus on the tool selection challenge for inference ...

Tensor Cores (TCUs) are specialized units first introduced by NVIDIA in the Volta microarchitecture in order to accelerate matrix multiplications for deep learning and linear algebra workloads. While these units have proved to be capable of providing ...

The influence of embedded systems is constantly growing. Increasingly powerful and versatile devices are developed and put on the market at a fast pace. Their functionality and number of features is increasing, and so are the constraints on the systems ...

The conventional approach of moving data to the CPU for computation has become a significant performance bottleneck for emerging scale-out data-intensive applications due to their limited data reuse. At the same time, the advancement ...

Pipelined image-based control uses parallel instances of its image-processing algorithm in a pipelined fashion to improve the quality of control. A performance-oriented control design improves the controller settling time with each additional processing ...