- RaiderChip introduces Generative AI Hardware Accelerator for low-cost FPGAs.
- GenAI v1 leverages Phi-2 LLM model on Versal FPGA with single Memory Controller.
- Utilizes 32-bit floating-point arithmetic for full precision without model modification.
- Offers real-time LLM inference speeds, outperforming CPU-based solutions by over 20%.
- IP core compatible with AMD Versal FPGA line-up and UltraScale Series devices.
- Flexible implementation across different FPGA vendor devices.
- Plug’n’play integration with minimal AXI interfaces, enhancing usability.
- FPGAs provide versatile options for local AI inference and accommodate rapid model updates.
Main AI News:
In a strategic move to redefine the landscape of Generative AI hardware acceleration, RaiderChip has launched its latest innovation, the Generative AI Hardware Accelerator, providing a turn-key solution for LLM inference now compatible with a diverse range of low-cost FPGA devices.
RaiderChip GenAI v1, powered by the Phi-2 LLM model and optimized for Versal FPGAs, boasts unmatched efficiency with a single Memory Controller, setting a new standard in AI hardware acceleration. Leveraging 32-bit floating-point arithmetic, RaiderChip’s design ensures full precision, enabling seamless utilization of original LLM model weights without necessitating any modification or quantization. This meticulous approach preserves the inherent intelligence and reasoning capabilities of the raw LLM models, aligning precisely with the creators’ vision.
The hallmark of RaiderChip’s GenAI v1 lies in its real-time AI LLM inference speeds, granting customers the ability to execute unquantized LLM models at full interactive speed. This efficiency edge is particularly notable in scenarios with limited memory bandwidth, where RaiderChip’s solution outperforms competitors by over 20%, presenting a significant leap from CPU-based inference alternatives.
Already available for FPGAs across the AMD Versal FPGA line-up and earlier UltraScale Series devices, RaiderChip’s GenAI v1 IP core stands as a versatile solution, adaptable to various FPGA vendor devices. This flexibility, combined with target-agnostic IP cores, empowers customers to tailor implementations according to their specific logic resource and inference speed requirements.
A key differentiator of RaiderChip’s offerings is the seamless integration facilitated by its plug’n’play IP cores, requiring only a minimal number of industry-standard AXI interfaces. By employing provided IP blocks, the GenAI v1 transforms into a user-friendly peripheral, fully controllable through customer software, enhancing accessibility and usability.
The introduction of FPGAs for Generative AI Acceleration expands the horizons for local AI inference of LLM models, presenting a compelling alternative to conventional approaches. Moreover, the reprogrammable nature of FPGAs positions them as ideal candidates within the dynamic landscape of AI innovation, accommodating swift adoption of new models and algorithmic upgrades with ease, ensuring scalability and longevity for deployed systems.
Conclusion:
RaiderChip’s launch of the Generative AI Hardware Accelerator marks a significant advancement in the market, providing a turn-key solution for low-cost FPGA devices. This innovation not only offers unparalleled efficiency and precision in AI inference but also underscores the adaptability and scalability of FPGAs in meeting the evolving demands of the AI landscape. As FPGAs become increasingly integral to AI acceleration, RaiderChip’s solution sets a new standard for performance, accessibility, and future-proofing in AI hardware.
