Dataflow Programming for Systems Design Space Exploration for Multicore Platforms
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The hardware complexity of modern machines makes the design of adequate programming models crucial for jointly ensuring performance, portability, and productivity in high-performance computing (HPC). Sequential task-based programming models paired with adv ...
Given the need for efficient high-performance computing, computer architectures combining CPUs, GPUs, and FPGAs are nowadays prevalent. However, each of these components suffers from electrical-level security risks. Moving to heterogeneous systems, with th ...
High-level synthesis (HLS) tools typically generate statically scheduled datapaths. Static scheduling implies that the resulting circuits have a hard time exploiting parallelism in code with potential memory dependences, with control dependences, or where ...
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In High-Level Synthesis (HLS), we consider abstractions that span from software to hardware and target heterogeneous architectures. Therefore, managing the complexity introduced by this is key to implementing good, maintainable, and extendible HLS compiler ...
High-Level Synthesis (HLS) tools generate hardware designs from high-level programming languages. These tools almost universally build datapaths that are controlled using a centralized controller which relies on a static, compile-time schedule to determine ...
We present a polarization-insensitive metasurface processor to perform spatial asymmetric filtering of an incident beam, thereby allowing for real-time parallel analog processing. To enable massive parallel processing, we introduce a multiple-input multipl ...
Timely insights lead to business growth and scientific breakthroughs but require analytical engines that cope with the ever-increasing data processing needs. Analytical engines relied on rapid CPU improvements, yet the end of Dennard scaling stopped the fr ...
Machine learning (ML) applications are ubiquitous. They run in different environments such as datacenters, the cloud, and even on edge devices. Despite where they run, distributing ML training seems the only way to attain scalable, high-quality learning. B ...