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Tempus framework offers scalable, resource-efficient GEMM for edge AI

By PulseAugur Editorial · [2 sources] ·

Researchers have developed Tempus, a new framework designed to optimize General Matrix Multiplication (GEMM) for edge AI deployments on AMD Versal SoCs. Unlike existing spatial scaling methods that fail on resource-constrained devices, Tempus uses a fixed compute block and temporal scaling through iterative execution and data tiling. This approach achieves significant performance gains, delivering 607 GOPS at 10.677W while demonstrating superior resource and power frugality compared to prior state-of-the-art methods. AI

IMPACT Enables more efficient LLM inference on resource-constrained edge devices by optimizing core matrix multiplication operations.

RANK_REASON Academic paper detailing a new framework for optimizing AI inference on edge hardware.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Tempus framework offers scalable, resource-efficient GEMM for edge AI

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Academic paper detailing a new framework for optimizing AI inference on edge hardware.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · M. Grailoo, J. N\'u\~nez-Y\'a\~nez ·

    Tempus: A Temporally Scalable Resource-Invariant GEMM Streaming Framework for Versal AI Edge

    arXiv:2605.00536v1 Announce Type: cross Abstract: Scaling laws for Large Language Models (LLMs) establish that model quality improves with computational scale, yet edge deployment imposes strict constraints on compute, memory, and power. Since General Matrix Multiplication (GEMM)…

  2. arXiv cs.LG TIER_1 English(EN) · J. Núñez-Yáñez ·

    Tempus: A Temporally Scalable Resource-Invariant GEMM Streaming Framework for Versal AI Edge

    Scaling laws for Large Language Models (LLMs) establish that model quality improves with computational scale, yet edge deployment imposes strict constraints on compute, memory, and power. Since General Matrix Multiplication (GEMM) accounts for up to 90\% of inference time, effici…

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