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Non-Parametric Dual-Manifold Mapping via 8-Bit Bounded Transformation Matrices: Challenging FP-centric Hardware Paradigms in Low-Energy AI

Lars Kopp 2026-06-14
Hardware AI

The paper addresses the problem of high energy costs from floating-point arithmetic in deep learning hardware. It proposes a non-parametric, training-free framework using 8-bit signed integer transformation matrices and bitwise logic for dual-manifold mapping. Experimental evidence shows near-perfect reconstruction under 90% truncation sparsity and 20% random node destruction, demonstrating extreme holographic resilience. This matters because it challenges the necessity of dense, floating-point-centric GPU accelerators, enabling a shift toward low-energy neuromorphic edge-computing.

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ITME: Inference Tiered Memory Expansion with Disaggregated CXL-Hybrid Memories

Hakbeom Jang, Younghoon Min, Sunwoong Kim, Taeyoung Ahn 2026-06-14
Inference ×

ITME addresses the problem of scaling shared context infrastructure for TB-scale LLM inference workloads beyond individual server capacity. The method leverages CXL-hybrid memory to provide massive, byte-addressable remote memory expansion, simplifying the software stack by eliminating complex software-level optimization. Experimental evidence from production-grade SK Hynix CMM and PCIe Gen5 NVMe SSDs, along with an FPGA prototype, shows up to a 35.7% throughput improvement over conventional CPU-offloading. This matters because ITME enables cost-efficient scaling of shared context layers for agentic and long-context LLMs by proactively managing data movement across the memory-storage hierarchy.

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