The AXIOM software layers

AXIOM project aims at developing a heterogeneous computing board (SMP-FPGA).The Software Layers developed at the AXIOM project are explained.OmpSs provides an easy way to execute heterogeneous codes in multiple cores. People and objects will soon share the same digital network for information exchange in a world named as the age of the cyber-physical systems. The general expectation is that people and systems will interact in real-time. This poses pressure onto systems design to support increasing demands on computational power, while keeping a low power envelop. Additionally, modular scaling and easy programmability are also important to ensure these systems to become widespread. The whole set of expectations impose scientific and technological challenges that need to be properly addressed.The AXIOM project (Agile, eXtensible, fast I/O Module) will research new hardware/software architectures for cyber-physical systems to meet such expectations. The technical approach aims at solving fundamental problems to enable easy programmability of heterogeneous multi-core multi-board systems. AXIOM proposes the use of the task-based OmpSs programming model, leveraging low-level communication interfaces provided by the hardware. Modular scalability will be possible thanks to a fast interconnect embedded into each module. To this aim, an innovative ARM and FPGA-based board will be designed, with enhanced capabilities for interfacing with the physical world. Its effectiveness will be demonstrated with key scenarios such as Smart Video-Surveillance and Smart Living/Home (domotics).Peer ReviewedPostprint (author's final draft

Real-time GPU-based face detection in HD video sequences

Modern GPUs have evolved into fully programmable parallel stream multiprocessors. Due to the nature of the graphic workloads, computer vision algorithms are in good position to leverage the computing power of these devices. An interesting problem that greatly benefits from parallelism is face detection. This paper presents a highly optimized Haar-based face detector that works in real time over high definition videos. The proposed kernel operations exploit both coarse and fine grain parallelism for performing integral image computations and filter evaluations, thus being beneficial not only for face detection but also for other computer vision techniques. Compared to previous implementations, the experiments show that our proposal achieves a sustained throughput of 35 fps under 1080p resolutions using a sliding window with step of one pixel.Peer Reviewe

Real-time GPU-based face detection in HD video sequences

Modern GPUs have evolved into fully programmable parallel stream multiprocessors. Due to the nature of the graphic workloads, computer vision algorithms are in good position to leverage the computing power of these devices. An interesting problem that greatly benefits from parallelism is face detection. This paper presents a highly optimized Haar-based face detector that works in real time over high definition videos. The proposed kernel operations exploit both coarse and fine grain parallelism for performing integral image computations and filter evaluations, thus being beneficial not only for face detection but also for other computer vision techniques. Compared to previous implementations, the experiments show that our proposal achieves a sustained throughput of 35 fps under 1080p resolutions using a sliding window with step of one pixel.Peer ReviewedPostprint (published version

Real-time GPU-based face detection in HD video sequences

Modern GPUs have evolved into fully programmable parallel stream multiprocessors. Due to the nature of the graphic workloads, computer vision algorithms are in good position to leverage the computing power of these devices. An interesting problem that greatly benefits from parallelism is face detection. This paper presents a highly optimized Haar-based face detector that works in real time over high definition videos. The proposed kernel operations exploit both coarse and fine grain parallelism for performing integral image computations and filter evaluations, thus being beneficial not only for face detection but also for other computer vision techniques. Compared to previous implementations, the experiments show that our proposal achieves a sustained throughput of 35 fps under 1080p resolutions using a sliding window with step of one pixel.Peer Reviewe

The AXIOM project (Agile, eXtensible, fast I/O Module)

Abstract-The AXIOM project (Agile, eXtensible, fast I/O Module) aims at researching new software/hardware architectures for the future Cyber-Physical Systems (CPSs). These systems are expected to react in real-time, provide enough computational power for the assigned tasks, consume the least possible energy for such task (energy efficiency), scale up through modularity, allow for an easy programmability across performance scaling, and exploit at best existing standards at minimal costs. Current solutions for providing enough computational power are mainly based on multi-or many-core architectures. For example, some current research projects (such as ADEPT or P-SOCRATES) are already investigating how to join efforts from the High-Performance Computing (HPC) and the Embedded Computing domains, which are both focused on high power efficiency, while GPUs and new Dataflow platforms such as Maxeler, or in general FPGAs, are claimed as the most energy efficient. We present the project's initial approach, ideas and key concepts, and describe the AXIOM preliminary architecture. Our starting point uses power efficient multi-core nodes, such as ARM cores and FPGA accelerators on the same die, as in the Xilinx Zynq. We will work to provide an integrated environment that supports programmability of the parallel, interconnected nodes that form a CPS system, and evaluate our ideas using demanding test application scenarios

The AXIOM project (Agile, eXtensible, fast I/O Module)

The AXIOM project (Agile, eXtensible, fast I/O Module) aims at researching new software/hardware architectures for the future Cyber-Physical Systems (CPSs). These systems are expected to react in real-time, provide enough computational power for the assigned tasks, consume the least possible energy for such task (energy efficiency), scale up through modularity, allow for an easy programmability across performance scaling, and exploit at best existing standards at minimal costs.Peer Reviewe

The AXIOM software layers

AXIOM project aims at developing a heterogeneous computing board (SMP-FPGA).The Software Layers developed at the AXIOM project are explained.OmpSs provides an easy way to execute heterogeneous codes in multiple cores. People and objects will soon share the same digital network for information exchange in a world named as the age of the cyber-physical systems. The general expectation is that people and systems will interact in real-time. This poses pressure onto systems design to support increasing demands on computational power, while keeping a low power envelop. Additionally, modular scaling and easy programmability are also important to ensure these systems to become widespread. The whole set of expectations impose scientific and technological challenges that need to be properly addressed.The AXIOM project (Agile, eXtensible, fast I/O Module) will research new hardware/software architectures for cyber-physical systems to meet such expectations. The technical approach aims at solving fundamental problems to enable easy programmability of heterogeneous multi-core multi-board systems. AXIOM proposes the use of the task-based OmpSs programming model, leveraging low-level communication interfaces provided by the hardware. Modular scalability will be possible thanks to a fast interconnect embedded into each module. To this aim, an innovative ARM and FPGA-based board will be designed, with enhanced capabilities for interfacing with the physical world. Its effectiveness will be demonstrated with key scenarios such as Smart Video-Surveillance and Smart Living/Home (domotics).Peer Reviewe