scispace - formally typeset
Search or ask a question

How does the use of synthesis techniques impact the performance and power consumption of FPGA-based systems? 


Best insight from top research papers

The use of synthesis techniques in FPGA-based systems has an impact on both performance and power consumption. Applying complex logic encryption techniques increases the complexity of the circuit, as well as the area and power consumption . Increasing the clock frequency of the application logic mildly increases the power consumption . On the other hand, using High-Level Synthesis (HLS) approach can significantly improve performance and reduce power consumption in FPGA-based systems . FPGA devices generally offer higher performance at lower power consumption compared to CPUs and GPUs . Therefore, the use of synthesis techniques, such as logic encryption and HLS, can have both positive and negative effects on the performance and power consumption of FPGA-based systems.

Answers from top 4 papers

More filters
Papers (4)Insight
The provided paper does not discuss the impact of synthesis techniques on the performance and power consumption of FPGA-based systems.
Proceedings ArticleDOI
Marsida Ibro, Galia Marinova 
01 Jun 2022
The provided paper does not specifically mention the impact of synthesis techniques on the performance and power consumption of FPGA-based systems.
The provided paper does not specifically mention the impact of synthesis techniques on the performance and power consumption of FPGA-based systems.
The provided paper does not discuss the impact of synthesis techniques on the performance and power consumption of FPGA-based systems.

Related Questions

How does the choice of microcontroller affect the quality and accuracy of direct digital synthesis?5 answersThe choice of microcontroller significantly impacts the quality and accuracy of direct digital synthesis (DDS). Different aspects of microcontrollers, such as clock frequency, DAC resolution, and architecture, play crucial roles in determining the performance of DDS systems. For instance, a microcontroller with a high clock frequency can enhance the frequency switching speed and dynamic range of the DDS system. Additionally, utilizing a nonlinear DAC in the DDS design can lead to reduced complexity and power dissipation, thereby improving accuracy and efficiency. Moreover, incorporating error feedback (EF) in DDS architectures can optimize output frequency accuracy by tuning EF filter coefficients based on the desired output frequency. Therefore, selecting an appropriate microcontroller with these considerations can significantly enhance the quality and accuracy of direct digital synthesis systems.
How much resources fpga uses for risc-v implementation?5 answersThe FPGA resource utilization for RISC-V implementations varies based on the specific design and architecture. Different studies have shown varying resource consumption levels. For instance, a study on a scalable soft Vector Processing Unit for FPGA based on RISC-V achieved significantly lower FPGA resource consumption compared to other vector processing units. Another study presented a 32-bit RISC-V soft processor that consumed about 43.7% of the FPGA device's resources for the whole processor circuit. Additionally, a 32-bit soft processor based on RISC-V instruction set was implemented with resource consumption increasing only by 7% compared to a commercial soft processor, showcasing a balance between high performance and low resource usage. Each implementation optimizes resource consumption differently to achieve the desired performance levels.
What are the current trends in the development of FPGA-based technologies for various applications?5 answersCurrent trends in FPGA-based technologies for various applications include the development of reconfigurable controllers for power electronics systems, the implementation of FIR filters for wireless communication standards in software-defined radio applications, and the acceleration of pose estimation frameworks for vision-based navigation using point-cloud and image registration algorithms. Additionally, there is a focus on analyzing the history of leading vendors in programmable logic device manufacturing, changes in development environments for FPGA projects, and the simplification of development processes through in-system programming. These trends highlight the versatility and efficiency of FPGA-based solutions across different domains, showcasing their adaptability to address specific challenges in power electronics, communication systems, navigation, and development environments.
What are the limitations of FPGA accelerators?4 answersFPGA accelerators have limitations in their application due to several factors. One limitation is the substantial computational burden associated with convolutional neural networks (CNNs), which limits their widespread use. Another limitation is the susceptibility of SRAM-based FPGAs to radiation effects, making them unsuitable for safety-critical applications. Additionally, the reliability of FPGA accelerators can be affected by hardware faults, such as single-event upsets (SEUs), which can cause accuracy loss and system corruptions. However, models that incorporate nonlinear activation and pooling layers tend to be more robust against these errors. For graph convolutional networks (GCNs), FPGA-based accelerators face challenges in terms of latency and energy efficiency, requiring further research in algorithm and hardware co-design, efficient task scheduling, higher generality, and faster development.
What are the challenges in using FPGAs in radar systems?4 answersFPGA-based radar systems face several challenges. One challenge is the limited performance of target detection due to the passive adoption of wireless channels by traditional radars. To address this, the use of reconfigurable intelligent surfaces (RIS) is proposed to improve detection accuracy by customizing channel conditions through phase shifts, known as MetaRadar. However, jointly optimizing both radar waveforms and RIS phase shifts to enhance multi-target detection performance is a complex task. Another challenge is the need for performance modeling to guide the compilation optimization process in FPGA designs for radar systems. These challenges highlight the importance of developing effective algorithms and tools to optimize radar waveforms, RIS phase shifts, and FPGA designs for improved radar system performance.
How can someone use fpga to speed up algorthm?5 answersField-Programmable Gate Arrays (FPGAs) can be used to speed up algorithms by taking advantage of their inherent parallelism and reprogrammability. FPGA implementations of algorithms have been shown to reduce execution time compared to software implementations. The use of FPGAs can provide a faster and more efficient alternative to executing numerical integration algorithms. Additionally, FPGAs can enhance security against software attacks by partitioning vulnerable programs between a general-purpose processor and an FPGA. To effectively utilize FPGAs for algorithm acceleration, a novel methodology and toolflow have been proposed, enabling efficient mapping of multiple applications onto heterogeneous FPGAs. This approach achieves faster application mapping and reduced memory usage compared to state-of-the-art methods. Overall, FPGAs offer a low-power and flexible accelerator option for speeding up algorithms, with potential applications in molecular dynamics simulations, numerical integration, and security enhancement.