17,988 research outputs found
Empowering parallel computing with field programmable gate arrays
After more than 30 years, reconļ¬gurable computing has grown from a concept to a mature ļ¬eld of science and technology. The cornerstone of this evolution is the ļ¬eld programmable gate array, a building block enabling the conļ¬guration of a custom hardware architecture. The departure from static von Neumannlike architectures opens the way to eliminate the instruction overhead and to optimize the execution speed and power consumption. FPGAs now live in a growing ecosystem of development tools, enabling software programmers to map algorithms directly onto hardware. Applications abound in many directions, including data centers, IoT, AI, image processing and space exploration. The increasing success of FPGAs is largely due to an improved toolchain with solid high-level synthesis support as well as a better integration with processor and memory systems. On the other hand, long compile times and complex design exploration remain areas for improvement. In this paper we address the evolution of FPGAs towards advanced multi-functional accelerators, discuss different programming models and their HLS language implementations, as well as high-performance tuning of FPGAs integrated into a heterogeneous platform. We pinpoint fallacies and pitfalls, and identify opportunities for language enhancements and architectural reļ¬nements
Programmable neural logic
Circuits of threshold elements (Boolean input, Boolean output neurons) have been shown to be surprisingly powerful. Useful functions such as XOR, ADD and MULTIPLY can be implemented by such circuits more efficiently than by traditional AND/OR circuits. In view of that, we have designed and built a programmable threshold element. The weights are stored on polysilicon floating gates, providing long-term retention without refresh. The weight value is increased using tunneling and decreased via hot electron injection. A weight is stored on a single transistor allowing the development of dense arrays of threshold elements. A 16-input programmable neuron was fabricated in the standard 2 Ī¼m double-poly, analog process available from MOSIS.
We also designed and fabricated the multiple threshold element introduced in [5]. It presents the advantage of reducing the area of the layout from O(n^2) to O(n); (n being the number of variables) for a broad class of Boolean functions, in particular symmetric Boolean functions such as PARITY.
A long term goal of this research is to incorporate programmable single/multiple threshold elements, as building blocks in field programmable gate arrays
Transformations of High-Level Synthesis Codes for High-Performance Computing
Specialized hardware architectures promise a major step in performance and
energy efficiency over the traditional load/store devices currently employed in
large scale computing systems. The adoption of high-level synthesis (HLS) from
languages such as C/C++ and OpenCL has greatly increased programmer
productivity when designing for such platforms. While this has enabled a wider
audience to target specialized hardware, the optimization principles known from
traditional software design are no longer sufficient to implement
high-performance codes. Fast and efficient codes for reconfigurable platforms
are thus still challenging to design. To alleviate this, we present a set of
optimizing transformations for HLS, targeting scalable and efficient
architectures for high-performance computing (HPC) applications. Our work
provides a toolbox for developers, where we systematically identify classes of
transformations, the characteristics of their effect on the HLS code and the
resulting hardware (e.g., increases data reuse or resource consumption), and
the objectives that each transformation can target (e.g., resolve interface
contention, or increase parallelism). We show how these can be used to
efficiently exploit pipelining, on-chip distributed fast memory, and on-chip
streaming dataflow, allowing for massively parallel architectures. To quantify
the effect of our transformations, we use them to optimize a set of
throughput-oriented FPGA kernels, demonstrating that our enhancements are
sufficient to scale up parallelism within the hardware constraints. With the
transformations covered, we hope to establish a common framework for
performance engineers, compiler developers, and hardware developers, to tap
into the performance potential offered by specialized hardware architectures
using HLS
- ā¦