Author's Latest Posts

Kahn Process Network: Parallel Programming Without Races And Non-Determinism
By Silexica - 18 Dec, 2019 - Comments: 0

Modern personal computing devices feature multiple cores. This is not only true for desktops, laptops, tablets and smartphones, but also for small embedded devices like the Raspberry Pi. In order to exploit the computational power of those platforms, application programmers are forced to write their code in a parallel way. Most often, they use the threading approach. This means multiple parts o... » read more

Accelerating Financial Applications With SLX FPGA
By Silexica - 25 Nov, 2019 - Comments: 0

This white paper demonstrates how engineers creating FPGA-based hardware accelerators for financial market models can take advantage of SLX FPGA. SLX FPGA can be used to accelerate optimization efforts for financial market models targeting option pricing. In this paper, two implementations of computation intensive models for pricing options are discussed, namely the Black-Scholes and Heston pri... » read more

Using SLX FPGA For Performance Optimization Of SHA-3 For HLS
By Silexica - 23 Oct, 2019 - Comments: 0

Author: Zubair Wadood SLX FPGA facilitates converting your C/C++ project into an FPGA bitstream easier and with higher performance. Leveraging standard HLS (High Level Synthesis) tools from FPGA vendors, SLX FPGA tackles the challenges associated with the HLS design flow. In this paper, the results of an SLX FPGA-optimized implementation of a Secure Hash Algorithm (SHA-3; also known as Kecca... » read more

Facilitating High Level Synthesis from MATLAB generated C C++
By Silexica - 21 Aug, 2019 - Comments: 0

MATLAB is the go-to toolbox for high level algorithm design in many application domains, ranging from signal processing to control systems and data analysis. MATLAB Coder generates executable C/C++ code from MATLAB implementations. However, the performance requirements of these applications often mandate a hardware implementation. SLX FPGA helps transform the auto-generated C/C++ code into a sy... » read more

Improving Execution Predictability On Linux With SLX
By Silexica - 24 Jul, 2019 - Comments: 0

For many applications, predictability and determinism are often times more desirable than raw performance. This is especially true in emerging markets, like cyber-physical systems or the internet-of-things. For many practical reasons, however, most engineers rely on Linux, which in multicore systems is usually neither predictable nor deterministic. This whitepaper analyzes the predictability of... » read more

Pushing Performance: Analysis and Optimization of Multicore Communication with SLX
By Silexica - 29 May, 2019 - Comments: 0

In theory, multicore programming should be simple: Tasks are placed on available cores and allocated a data buffer in the shared memory to communicate data between two tasks. However, the amount of communication resources in the latest multicore SoC is very limited. One cannot deal with all the data communications required by all the tasks without being able to understand communication conte... » read more

SLX Multi-Objective Optimization (MOPT)
By Silexica - 24 Apr, 2019 - Comments: 0

Technologies such as autonomous cars and 5G communication are seeing a rapid increase in the number of processing elements (PE) per platform. Where software professionals were used to programming one, two or a handful of cores, the game has now changed. Intel’s Many Integrated Core Architecture [3] contains up to 78 cores, Nvidia Tegra XI[2] has up to 260 cores and Adapteva’s Epiphany-V[1] ... » read more

Multicore Software Design For An LTE Base Station
By Silexica - 27 Mar, 2019 - Comments: 0

This paper presents a typical base station design scenario, where decisions about HW/SW partitioning, the number of processing elements, and operational system parameters, among other things, need to be made early on by system architects. SLX determines the impact of these various design decisions and parameter selections, while exploring different target architecture configurations and checks ... » read more

Optimizing Deep-Learning Inference For Embedded Devices
By Silexica - 27 Feb, 2019 - Comments: 0

Deep artificial neural networks (ANNs) have emerged as universal feature extractors in various tasks as they approach (and in many cases surpass) human-level performance. They have become fundamental building blocks of almost every modern artificially intelligent (AI) application, from online shop recommendations to self-driving cars. This whitepaper highlights how different challenges relat... » read more

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