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The paper introduces the development of a modular compiler for a subset of a C-like language, which addresses the challenges in constructing a compiler for high-level languages. This modular approach will allow developers to modify a…

We propose a novel computing runtime that exposes remote compute devices via the cross-vendor open heterogeneous computing standard OpenCL and can execute compute tasks on the MEC cluster side across multiple servers in a scalable manner.…

Reconfigurable computing offers a good balance between flexibility and energy efficiency. When combined with software-programmable devices such as CPUs, it is possible to obtain higher performance by spatially distributing the…

The emergence of heterogeneity and domain-specific architectures targeting deep learning inference show great potential for enabling the deployment of modern CNNs on resource-constrained embedded platforms. A significant development is the…

Domain-Specific architectures with accelerators for machine learning and signal processing require efficient bulk data movement and high-bandwidth access to large datasets. Such capabilities are often absent from minimal open-source…

Caribou is a versatile data acquisition system used in multiple collaborative frameworks (CERN EP R&D, DRD3, AIDAinnova, Tangerine) for laboratory and test-beam qualification of novel silicon pixel detector prototypes. The system is built…

An overview is given of the QCDOC architecture, a massively parallel and highly scalable computer optimized for lattice QCD using system-on-a-chip technology. The heart of a single node is the PowerPC-based QCDOC ASIC, developed in…

As memory increasingly dominates system cost and energy, heterogeneous on-chip memory systems that combine technologies with complementary characteristics are becoming essential. Gain Cell RAM (GCRAM) offers higher density, lower power, and…

We present a DevIce-to-System Performance EvaLuation (DISPEL) workflow that integrates transistor and interconnect modeling, parasitic extraction, standard cell library characterization, logic synthesis, cell placement and routing, and…

Materials exhibiting a substitutional disorder such as multicomponent alloys and mixed metal oxides/oxyfluorides are of great importance in many scientific and technological sectors. Disordered materials constitute an overwhelmingly large…

We present a portable platform, called PIC_ENGINE, for accelerating Particle-In-Cell (PIC) codes on heterogeneous many-core architectures such as Graphic Processing Units (GPUs). The aim of this development is efficient simulations on…

Embedded systems are playing an increasingly important role in control engineering. Despite their popularity, embedded systems are generally subject to resource constraints and it is therefore difficult to build complex control systems on…

Big data streaming applications require utilization of heterogeneous parallel computing systems, which may comprise multiple multi-core CPUs and many-core accelerating devices such as NVIDIA GPUs and Intel Xeon Phis. Programming such…

We propose that the Matrix Profile data structure, conventionally applied to large scale time-series data mining, is applicable to the analysis and suppression of cyclical error in electromechanical systems, paving the way for an…

SRAM-based compute-in-memory (CIM) offers high computational density and energy efficiency for deep neural network (DNN) accelerators, but its limited capacity causes on/off-chip data movement overhead for large DNN models. Existing CIM…

This thesis (extended abstract) presents the software development efforts toward efficient exploitation of heterogeneity through intricate mapping of computational kernels, collaborative execution of multiple processing elements and…

While coarse-grained reconfigurable arrays (CGRAs) have emerged as promising programmable accelerator architectures, pipelining applications running on CGRAs is required to ensure high maximum clock frequencies. Current CGRA compilers…

Embedded systems have proliferated in various consumer and industrial applications with the evolution of Cyber-Physical Systems and the Internet of Things. These systems are subjected to stringent constraints so that embedded software must…

The SALSA chip is a future readout ASIC foreseen for the MPGD detectors, developed in the framework of the EIC collider project, to equip the MPGD trackers of the EPIC experiment. It is designed to be versatile, to be adapted to other…

Stencil computation is one of the most important kernels in various scientific computing. Nowadays, most Stencil-driven scientific computing still relies heavily on supercomputers, suffering from expensive access, poor scalability, and…