Related papers: Escaping Flatland: A Placement Flow for Enabling 3…

Structural vibrations are a source of unwanted noise in engineering systems like cars, trains or airplanes. Minimizing these vibrations is crucial for improving passenger comfort. This work presents a novel design optimization approach…

Point-based 3D point cloud models employ computation and memory intensive mapping functions alongside NN layers for classification/segmentation, and are executed on server-grade GPUs. The sparse, and unstructured nature of 3D point cloud…

It is highly predicted that the roads and parking areas will be extremely congested with vehicles to the point that searching for a novel solution will not be an optional choice for conserving the sustainability rate of the overall…

Engineering is an important domain for supercomputing, with the Alya model being a popular code for undertaking such simulations. With ever increasing demand from users to model larger, more complex systems at reduced time to solution it is…

Long Short-term Memory Networks (LSTMs) are a vital Deep Learning technique suitable for performing on-device time series analysis on local sensor data streams of embedded devices. In this paper, we propose a new hardware accelerator design…

Field-programmable gate arrays (FPGAs) are widely used to implement deep learning inference. Standard deep neural network inference involves the computation of interleaved linear maps and nonlinear activation functions. Prior work for…

We investigate the feasibility of real-time 3D Gaussian Splatting (3DGS) rasterisation on edge clients with varying Gaussian splat counts and GPU computational budgets. Instead of evaluating multiple physical devices, we adopt an…

Layer assignment is critical for global routing of VLSI circuits. It converts 2D routing paths into 3D routing solutions by determining the proper metal layer for each routing segments to minimize congestion and via count. As different…

A variety of computing platform like Field Programmable Gate Array (FPGA), Graphics Processing Unit (GPU) and multicore Central Processing Unit (CPU) in data centers are suitable for acceleration of data-intensive workloads. Especially,…

Modern OLAP systems have mitigated I/O bottlenecks via storage-compute separation and columnar layouts, but CPU costs in the execution layer (especially Top-K selection and join probe) are emerging as new bottlenecks at scale. This paper…

In traditional EDA flows, layout-level performance metrics are only obtainable after placement and routing, hindering global optimization at earlier stages. Although some neural-network-based solutions predict layout-level performance…

FPGAs are going mainstream. Major companies that were not traditionally FPGA-focused are now seeking ways to exploit the benefits of reconfigurable technology and provide it to their customers. In order to do so, a debug ecosystem that…

Hardware acceleration of algorithms is an effective method for improving performance in high-demand computational tasks. However, developing hardware designs for such acceleration fundamentally differs from software development, as it…

In traditional topology optimization, the computing time required to iteratively update the material distribution within a design domain strongly depends on the complexity or size of the problem, limiting its application in real engineering…

We propose a flat, analytic, mixed-size placement algorithm ePlace-3D for three-dimension integrated circuits (3D-ICs) using nonlinear optimization. Our contributions are (1) electrostatics based 3D density function with globally uniform…

An efficient hardware implementation for Simultaneous Localization and Mapping (SLAM) methods is of necessity for mobile autonomous robots with limited computational resources. In this paper, we propose a resource-efficient FPGA…

Real-time embedded platforms with resource constraints can take the benefits of mixed-criticality system where applications with different criticality-level share computational resources, with isolation in the temporal and spatial domain. A…

3D Gaussian Splatting (3DGS) has emerged as a promising 3D reconstruction technique. The traditional 3DGS training pipeline follows three sequential steps: Gaussian densification, Gaussian projection, and color splatting. Despite its…

Networked embedded systems typically leverage a collection of low-power embedded systems (nodes) to collaboratively execute applications spanning diverse application domains (e.g., video, image processing, communication, etc.) with diverse…

When trained as generative models, Deep Learning algorithms have shown exceptional performance on tasks involving high dimensional data such as image denoising and super-resolution. In an increasingly connected world dominated by mobile and…