Acceleration by Inline Cache for Memory-Intensive Algorithms on FPGA via High-Level Synthesis

Using FPGA-based acceleration of high-performance computing (HPC) applications to reduce energy and power consumption is becoming an interesting option, thanks to the availability of high-level synthesis (HLS) tools that enable fast design cycles. However, obtaining good performance for memory-intensive algorithms, which often exchange large data arrays with external DRAM, still requires time-consuming optimization and good knowledge of hardware design. This article proposes a new design methodology, based on dedicated application- and data array-specific caches. These caches provide most of the benefits that can be achieved by coding optimized DMA-like transfer strategies by hand into the HPC application code, but require only limited manual tuning (basically the selection of architecture and size), are neutral to target HLS tool and technology (FPGA or ASIC), and do not require changes to application code. We show experimental results obtained on five common memory-intensive algorithms from very diverse domains, namely machine learning, data sorting, and computer vision. We test the cost and performance of our caches against both out-of-the-box code originally optimized for a GPU, and manually optimized implementations specifically targeted for FPGAs via HLS. The implementation using our caches achieved an 8X speedup and 2X energy reduction on average with respect to out-of-the-box models using only simple directive-based optimizations (e.g., pipelining). They also achieved comparable performance with much less design effort when compared with the versions that were manually optimized to achieve efficient memory transfers specifically for an FPGA

Low power and high performance heterogeneous computing on FPGAs

L'abstract è presente nell'allegato / the abstract is in the attachmen

Performance Optimization of Memory Intensive Applications on FPGA Accelerator

L'abstract è presente nell'allegato / the abstract is in the attachmen

Array-specific dataflow caches for high-level synthesis of memory-intensive algorithms on FPGAs

Designs implemented on field-programmable gate arrays (FPGAs) via high-level synthesis (HLS) suffer from off-chip memory latency and bandwidth bottlenecks. FPGAs can access both large but slow off-chip memories (DRAM), and fast but small on-chip memories (block RAMs and registers). HLS tools allow exploiting the memory hierarchy in a scratchpad-like fashion, requring a significant manual effort. We propose an automation of the FPGA memory management in Xilinx Vitis HLS through a fully- configurable C++ source-level cache. Each DRAM-mapped array can be associated with a private level 2 (L2) cache with one or more ports, and each port can optionally provide level 1 cache. The L2 cache runs in a separate dataflow task with respect to the application accessing it. This solution isolates off-chip memory accesses and data buffering into dedicated dataflow tasks, resembling the load, compute, store design paradigm, but without the drawback of manual algorithm refactoring. Experimental results collected from FPGA board show that our cache speeds up the execution of a variety of benchmarks by up to 60 times compared to the out-of-the-box solution provided by HLS, requiring very limited optimization effort. Our caches are not meant to compete with manually optimized implementations quality of results (QoR), but rather to significantly save design effort, in exchange for some QoR, to make the HLS flow a bit more software-like, allowing the designer to focus on algorithmic optimizations, rather than on explicit memory management. Moreover, caching could be the only feasible memory optimization for algorithms with data-dependent or irregular memory access patterns, but with good data locality

Enhancing an embedded processor core for efficient and isolated execution of cryptographic algorithms

We propose enhancing a reconfigurable and extensible embedded RISC processor core with a protected zone for isolated execution of cryptographic algorithms. The protected zone is a collection of processor subsystems such as functional units optimized for high-speed execution of integer operations, a small amount of local memory for storing sensitive data during cryptographic computations, and special-purpose and cryptographic registers to execute instructions securely. We outline the principles for secure software implementations of cryptographic algorithms in a processor equipped with the proposed protected zone. We demonstrate the efficiency and effectiveness of our proposed zone by implementing the most-commonly used cryptographic algorithms in the protected zone; namely RSA, elliptic curve cryptography, pairing-based cryptography, AES block cipher, and SHA-1 and SHA-256 cryptographic hash functions. In terms of time efficiency, our software implementations of cryptographic algorithms running on the enhanced core compare favorably with equivalent software implementations on similar processors reported in the literature. The protected zone is designed in such a modular fashion that it can easily be integrated into any RISC processor. The proposed enhancements for the protected zone are realized on an FPGA device. The implementation results on the FPGA confirm that its area overhead is relatively moderate in the sense that it can be used in many embedded processors. Finally, the protected zone is useful against cold-boot and micro-architectural side-channel attacks such as cache-based and branch prediction attacks

Just-in-time Hardware generation for abstracted reconfigurable computing

This thesis addresses the use of reconfigurable hardware in computing platforms, in order to harness the performance benefits of dedicated hardware whilst maintaining the flexibility associated with software. Although the reconfigurable computing concept is not new, the low level nature of the supporting tools normally used, together with the consequent limited level of abstraction and resultant lack of backwards compatibility, has prevented the widespread adoption of this technology. In addition, bandwidth and architectural limitations, have seriously constrained the potential improvements in performance. A review of existing approaches and tools flows is conducted to highlight the current problems being faced in this field. The objective of the work presented in this thesis is to introduce a radically new approach to reconfigurable computing tool flows. The runtime based tool flow introduces complete abstraction between the application developer and the underlying hardware. This new technique eliminates the ease of use and backwards compatibility issues that have plagued the reconfigurable computing concept, and could pave the way for viable mainstream reconfigurable computing platforms. An easy to use, cycle accurate behavioural modelling system is also presented, which was used extensively during the early exploration of new concepts and architectures. Some performance improvements produced by the new reconfigurable computing tool flow, when applied to both a MIPS based embedded platform, and the Cray XDl, are also presented. These results are then analyzed and the hardware and software factors affecting the performance increases that were obtained are discussed, together with potential techniques that could be used to further increase the performance of the system. Lastly a heterogenous computing concept is proposed, in which, a computer system, containing multiple types of computational resource is envisaged, each having their own strengths and weaknesses (e.g. DSPs, CPUs, FPGAs). A revolutionary new method of fully exploiting the potential of such a system, whilst maintaining scalability, backwards compatibility, and ease of use is also presented