arXiv:2606.11117v1 Announce Type: cross Abstract: Designing FPGA-based accelerators for modern artificial intelligence workloads requires exploring a large and complex hardware design space that involves architectural parameters, data flow strategies, and memory hierarchies, making the process very time consuming. While existing methodologies such as SECDA enable rapid hardware-software co-design through SystemC simulation and FPGA execution, identifying efficient accelerator configurations remains a largely manual process requiring extensive domain knowledge. SECDA-DSE is a framework that int
The increasing complexity and demand for specialized hardware in AI workloads, particularly with FPGA-based accelerators, necessitates more efficient and automated design processes.
Automating FPGA accelerator design reduces development time and expertise requirements, making high-performance custom hardware more accessible and efficient for AI applications.
The manual and time-consuming process of optimizing FPGA configurations for AI workloads is becoming increasingly automated, potentially democratizing access to high-performance custom hardware.
- · AI accelerator developers
- · FPGA manufacturers
- · Cloud computing providers
- · Manual hardware design firms
- · General-purpose CPU/GPU reliance
Faster and more efficient deployment of AI models on specialized hardware accelerating AI development cycles.
Reduced barriers to entry for developing custom AI hardware, fostering innovation outside of major chip manufacturers.
Enhanced performance and energy efficiency for AI at the edge and in data centers, impacting compute supply chains and energy consumption.
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Read at arXiv cs.AI