DAE4HLS enables explicit decoupling of access and execute in HLS to unlock memory-level parallelism, delivering 10-79x speedups for complex workloads on commercial and dynamic HLS tools.
Wulf and Sally A
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LLM inference should be reframed and evaluated as energy-to-token production with a Token Production Function that accounts for power, cooling, and efficiency ceilings.
Modern benchmarks confirm that region-based custom allocators retain locality advantages over state-of-the-art general-purpose allocators, extending the original 2000 conclusions with new applications and fragmentation analysis.
citing papers explorer
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DAE4HLS: Exposing Memory-Level Parallelism for High-Level Synthesis using Explicit Decoupling
DAE4HLS enables explicit decoupling of access and execute in HLS to unlock memory-level parallelism, delivering 10-79x speedups for complex workloads on commercial and dynamic HLS tools.
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Position: LLM Inference Should Be Evaluated as Energy-to-Token Production
LLM inference should be reframed and evaluated as energy-to-token production with a Token Production Function that accounts for power, cooling, and efficiency ceilings.
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Reconsidering "Reconsidering Custom Memory Allocation"
Modern benchmarks confirm that region-based custom allocators retain locality advantages over state-of-the-art general-purpose allocators, extending the original 2000 conclusions with new applications and fragmentation analysis.