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Verification and testing of hardware heavily relies on cycle-accurate simulation of RTL. As single-processor performance is growing only slowly, conventional, single-threaded RTL simulation is becoming impractical for increasingly complex chip designs and ...

The recent rise in interest in point clouds as an imaging modality has motivated standardization groups such as JPEG and MPEG to launch activities aiming at developing compression standards for point clouds. Lossy compression usually introduces visual arti ...

Electronic devices play an irreplaceable role in our lives. With the tightening time to market, exploding demand for computing power, and continuous desire for smaller, faster, less energy-consuming, and lower-cost chips, computer-aided design for electron ...

Driven by the demand for real-time processing and the need to minimize latency in AI algorithms, edge computing has experienced remarkable progress. Decision-making AI applications stand out for their heavy reliance on data-centric operations, predominantl ...

Machine learning algorithms such as Convolutional Neural Networks (CNNs) are characterized by high robustness towards quantization, supporting small-bitwidth fixed-point arithmetic at inference time with little to no degradation in accuracy. In turn, small ...

The invention relates to an Imaging sensor device in a stacked arrangement comprising: - a pixel array tier comprising a plurality of pixel segments each having a plurality of pixels for photon detection each providing a digital pixel output; - a processin ...

The increasing complexity of transformer models in artificial intelligence expands their computational costs, memory usage, and energy consumption. Hardware acceleration tackles the ensuing challenges by designing processors and accelerators tailored for t ...

Cloud computing environments increasingly provision field-programmable gate arrays (FPGAs) for their programmability and hardware-level parallelism. While FPGAs are typically used by one tenant at a time, multitenant schemes supporting spatial sharing of c ...

Video DeepFakes are fake media created with Deep Learning (DL) that manipulate a person’s expression or identity. Most current DeepFake detection methods analyze each frame independently, ignoring inconsistencies and unnatural movements between frames. Som ...

Today's continued increase in demand for processing power, despite the slowdown of Moore's law, has led to an increase in processor count, which has resulted in energy consumption and distribution problems. To address this, there is a growing trend toward ...

Dataflow programming is a methodology that enables the development of high-level, parametric programs that are independent of the underlying platform. This approach is particularly useful for heterogeneous platforms, as it eliminates the need to rewrite ap ...

The demise of Moore's Law and Dennard scaling has resulted in diminishing performance gains for general-purpose processors, and so has prompted a surge in academic and commercial interest for hardware accelerators. Specialized hardware has already redefine ...

Embedded systems confront two opposite goals: low-power operation and high performance. The current trend to reach these goals is toward heterogeneous platforms, including multi-core architectures with heterogeneous cores and hardware accelerators. The lat ...

The miniaturization of integrated circuits (ICs) and their higher performance and energy efficiency, combined with new machine learning algorithms and applications, have paved the way to intelligent, interconnected edge devices. In the medical domain, they ...

Omnidirectional video streaming is usually implemented based on the representations of tiles, where the tiles are obtained by splitting the video frame into several rectangular areas and each tile is converted into multiple representations with different r ...

Modern hardware is increasingly complex, requiring increasing effort to understand in order to carefully engineer systems for optimal performance and effective utilization. Moreover, established design principles and assumptions are not portable to modern ...

Modern data management systems aim to provide both cutting-edge functionality and hardware efficiency. With the advent of AI-driven data processing and the post-Moore Law era, traditional memory-bound scale-up data management operations face scalability ch ...

Machine learning and data processing algorithms have been thriving in finding ways of processing and classifying information by exploiting the hidden trends of large datasets. Although these emerging computational methods have become successful in today's ...

The use of point clouds as an imaging modality has been rapidly growing, motivating research on compression methods to enable efficient transmission and storage for many applications. While compression standards relying on conven- tional techniques such as ...

With Moore's law coming to an end, increasingly more hope is being put in specialized hardware implemented on reconfigurable architectures such as Field-Programmable Gate Arrays (FPGAs). Yet, it is often neglected that these architectures themselves experi ...

Groupe SCI STI MM

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Highly Parallel RTL Simulation

Subjective performance evaluation of bitrate allocation strategies for MPEG and JPEG Pleno point cloud compression

Contemporary Logic Synthesis: with an Application to AQFP Circuit Optimization

EdgeAI-Aware Design of In-Memory Computing Architectures

DBFS: Dynamic Bitwidth-Frequency Scaling for Efficient Software-defined SIMD

Imaging sensor device using an array of single-photon avalanche diode photodetectors

Accelerator-driven Data Arrangement to Minimize Transformers Run-time on Multi-core Architectures

X-Attack 2.0: The Risk of Power Wasters and Satisfiability Don’t-Care Hardware Trojans to Shared Cloud FPGAs

Efficient Temporally-Aware DeepFake Detection using H.264 Motion Vectors

Compilation and Design Space Exploration of Dataflow Programs for Heterogeneous CPU-GPU Platforms

Design Space Exploration for Partitioning Dataflow Program on CPU-GPU Heterogeneous System

Building Chips Faster: Hardware-Compiler Co-Design for Accelerated RTL Simulation

Acceleration of Control Intensive Applications on Coarse-Grained Reconfigurable Arrays for Embedded Systems

Exploring brain-inspired multi-core heterogeneous hardware templates for low-power biomedical embedded systems

Quality-Constrained Encoding Optimization for Omnidirectional Video Streaming

Chaosity: Understanding Contemporary NUMA-architectures

Post-Moore's Law Fusion: High-Bandwidth Memory, Accelerators, and Native Half-Precision Processing for CPU-Local Analytics

2D Nanosystems: Applications of 2D Semiconductors for In-Memory Computing

Evaluating the effect of sparse convolutions on point cloud compression

Automating the Design of Programmable Interconnect for Reconfigurable Architectures