Practical AI Software and hardware acceleration with Groq
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Apr 2, 2025 
Dhananjay Singh, a Staff Machine Learning Engineer at Groq, discusses the cutting-edge advancements in AI acceleration. He reveals how Groq optimizes AI inference by focusing on software-first design, setting a new standard against traditional GPU architectures. The conversation dives into the integration of hardware and software for superior performance, showcasing practical applications like a snowboarding navigation system. Lastly, Singh touches on the importance of edge computing and the evolving landscape of physical AI, highlighting the challenges and innovations within the field.
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Deterministic AI Acceleration
- Groq prioritizes deterministic compute and networking for faster AI inference.
- Their software compiler schedules each operation, minimizing delays like stop signs on a road.
Software-First Hardware Design
- Groq developed its software compiler before designing its hardware (Groq LPU).
- This software-first approach addresses hardware inefficiencies for optimized performance.
Compiler vs. Kernels
- Traditional GPU kernels require extensive engineering and contend with memory hierarchies.
- Groq's compiler-based approach eliminates kernels, providing fine-grained control over scheduling and minimizing data transfer delays.