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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ReSET: Accurate Latency-Critical NVFP4 Reasoning via Step-Aware Temperature Scaling

Sihwa Lee, Janghwan Lee, Donghoon Yoo, Jae Gon Kim 2026-06-14

Inference Quantization Hardware

Problem: Large reasoning models (LRMs) incur high inference costs due to long reasoning traces, and directly applying NVFP4 low-precision quantization degrades reasoning accuracy while existing kernels fail to deliver latency benefits in small-batch autoregressive decoding. Method: ReSET proposes a step-aware temperature scaling method that estimates step-level uncertainty online using both token-level and step-level entropy signals, and introduces a CUDA-core small-M NVFP4 kernel for latency-critical decoding. Finding: ReSET improves NVFP4 reasoning accuracy by up to ~2 points over the NVFP4 baseline, and the custom kernel achieves up to 2.5× kernel-level speedup over NVFP4 vLLM and approximately 2× end-to-end decoding speedup over BF16. Why it matters: This work enables accurate and efficient low-precision inference for latency-critical LRM deployments, reducing computational and memory costs without sacrificing reasoning quality.

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Harnessing Routing Foresight for Micro-step-level MoE load balancing in RL Post-training

Yuming Zhou, Haoyang Li, Sheng Lin, Yanfeng Zhao 2026-06-14

Language Model LLM Training GPU Hardware

ForeMoE addresses expert load imbalance in Mixture-of-Experts (MoE) models during reinforcement learning (RL) post-training, where existing step-level statistics fail due to high-frequency micro-step fluctuations. The method exploits foreseeable routing information from the rollout stage to proactively guide load balancing, using a hierarchical planner to decompose the NP-hard problem and a transfer engine for overlapped expert transfer. Evaluations on 64 GPUs show up to a 1.45× speedup over state-of-the-art RL post-training systems. This matters because it enables efficient scaling of MoE LLMs under the unique workload dynamics of RL post-training, a dominant paradigm in current LLM development.

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Structured Testbench Generation for LLM-Driven HDL Design and Verification-Oriented Data Curation

En-Ming Huang, Yu-Hung Kao, Ren-Hao Deng, Wei-Po Hsin 2026-06-14

LLM Design Data

Problem: Automated testbench generation is a bottleneck in LLM-driven RTL workflows due to stochastic, costly, and low-coverage outputs from prompt-based methods. Method: STG (Structured Testbench Generation) exploits hardware design structure to produce deterministic testbenches. Finding: STG runs 720x faster than iterative LLM-based flows, achieves higher coverage, reduces false-pass verdicts, and is 11x faster and 127x more energy-efficient than LLM-based filtering on a single CPU core. Why it matters: STG enables rapid, reliable verification for LLM-driven design, improves RTL benchmarks by exposing faulty testbenches, and yields state-of-the-art distilled models with reduced node count.

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Maestro: Workload-Aware Cross-Cluster Scheduling for LLM-Based Multi-Agent Systems

Jinghao Wang, Xiao Zhou, Xiaoyang Sun, Yihui Zhang 2026-06-14

Workload Scheduling LLM Agent

Maestro addresses the problem of high resource consumption and scheduling inefficiencies in deploying LLM-based multi-agent systems under strict GPU budgets. The method uses agent semantics to predict output length and memory usage, enabling hierarchical scheduling with dynamic model co-location, latency-aware routing, and workflow-aware prioritization. Experimental evidence shows Maestro reduces KV-reservation HBM by 67.2% and improves high-contention SLO attainment over EDF by 23.6 percentage points. This matters because it enables efficient, scalable deployment of complex multi-agent workflows in resource-constrained cloud environments.

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SupraSNN: Exploiting Synapse-Level Parallelism in Spiking Neural Network Accelerators through Co-Optimized Mapping and Scheduling

Seyed Sadra Ghavami, Mohammad Hossein Nikkhah, Mohammad Rasoul Roshanshah, Saeed Safari 2026-06-14

Parallelism Neural Network Accelerator Scheduling

The problem is that deploying Spiking Neural Networks (SNNs) on hardware is limited by the challenge of managing massive parallelism, analogous to the historical barrier of serial execution in processors. The method introduces SupraSNN, a superscalar-inspired hardware-software co-design framework that treats synaptic events as parallelizable micro-operations, using a Multi-Cast Tree, parallel Synapse Processing Units, and a Merge Tree with a unified Neuron Unit. Experimental evidence shows that on a Xilinx Zynq XC7Z020 FPGA, SupraSNN achieves 149 μs inference latency and 0.025 mJ per image for MNIST (93.44% accuracy), delivering 47.6% lower latency and 5.6× better energy efficiency than prior FPGA-based SNN accelerators. This matters because it demonstrates a practical path to high synapse-level parallelism and energy efficiency for SNN deployment, extending to recurrent SNNs on the Spiking Heidelberg Dataset.

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Characterizing Software Aging in GPU-Based LLM Serving Systems

Domenico Cotroneo, Bojan Cukic 2026-06-14

GPU LLM Serving

The paper addresses the problem of software aging in GPU-based LLM serving systems, which differ from traditional CPU-centric systems due to heterogeneous hardware and highly variable workloads. The method involves a 216-hour empirical campaign across six co-located deployments with identical stress, monitoring host, device, and client metrics and applying a statistical pipeline for autocorrelation and multiple testing. Experimental evidence shows statistically significant memory aging in all deployments, with leak rates strongly dependent on the serving runtime and configuration. This matters because it provides a reproducible framework bridging software aging and rejuvenation research with LLM serving, enabling future mitigation strategies.

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An LLM System for Autonomous Variational Quantum Circuit Design

Kenya Sakka, Wataru Mizukami, Kosuke Mitarai 2026-06-14

Language Model LLM Machine Learning Hardware Quant Ph

The problem is that designing high-performing quantum circuits remains heavily reliant on human expertise. The method introduces an autonomous agentic framework using LLMs with seven integrated components for iterative circuit design under explicit constraints. Experimental evidence shows the framework outperforms representative quantum feature maps on image classification and achieves competitive accuracy for molecular ground state estimation across seven molecules. This matters because it establishes LLM-driven agentic systems as a viable paradigm for automated quantum circuit design and demonstrates AI's role in iterative scientific optimization.

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Attention by Synchronization in Coupled Oscillator Networks

Fabio Pasqualetti, Taosha Guo 2026-06-14

Attention Language Model Training Transformer Hardware Network

The problem is that softmax attention requires exponentiation and global reduction, which are energy-expensive on von Neumann hardware and lack a natural physical analog. The method replaces softmax with Kuramoto synchronization dynamics, where queries are fixed anchors on a sphere and free oscillators equilibrate to encode attention weights via cosine similarity. Experimental evidence shows that at oscillator dimension 2, oscillator attention outperforms softmax on keyword spotting (+1.00 pp) and subject-verb agreement (+5.27 pp), while on causal language modeling it closes the perplexity gap as dimension increases, from +11.09 to +2.98 on WikiText-2 and from +2.39 to +0.57 on TinyStories. This matters because it provides a mathematically grounded blueprint for accurate attention on energy-constrained physical substrates without requiring exponentiation or global reduction.

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nomp: A Framework for Building Domain Specific Compilers

Thilina Ratnayaka, Kaushik Kulkarni, Nipuna Fernando, Pubudu Hewavitharana 2026-06-14

OpenMP CUDA HPC Compiler Runtime

Problem: Existing GPU programming models force a trade-off between low-level performance and high-level productivity, with no single solution achieving all three goals of productivity, portability, and performance. Method: The authors propose nomp, a framework for building domain-specific compilers that uses a pragma-based programming model and a runtime for code transformation and generation based on user-provided metadata. Finding or experimental evidence: The abstract does not disclose experimental results. Why it matters: nomp aims to improve programmer productivity without sacrificing performance or portability by enabling reuse of domain-specific optimization patterns.

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ReSCom: A Reconfigurable Spiking Neural Network Accelerator Using Stochastic Computing

Ali Alipour Fereidani, Mohammad Rasoul Roshanshah, Saeed Safari 2026-06-14

Neural Network Accelerator

ReSCom addresses the high power and area costs of Spiking Neural Network (SNN) hardware by introducing a reconfigurable accelerator that uses stochastic computing for multiplication while preserving exact fixed-point addition and subtraction. The method employs a unified neuron design supporting IF, LIF, and Synaptic models, enabling runtime trade-offs between accuracy, latency, and energy. On MNIST inference with a Xilinx Artix-7 FPGA, ReSCom achieves 92.80% accuracy at 0.05 mJ per image and 100 MHz, outperforming recent state-of-the-art implementations in energy efficiency. This matters because it demonstrates that stochastic computing can stabilize SNN inference while providing explicit, dynamic control over accuracy-latency-energy trade-offs for resource-constrained edge applications.

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Work Stealing for the 2D-Mesh Topology of Satellite Constellations in Low Earth Orbit

Mia Reitz, Dorian Chenet, Jonas Posner 2026-06-14

High × Performance Computing HPC Compiler Runtime

The problem is that existing Asynchronous Many-Task (AMT) runtimes assume a fully connected network with low, uniform latency, which is invalid for satellite constellations in Low Earth Orbit (LEO) that communicate via a sparse mesh topology. The method proposes a neighbor-only work stealing strategy where workers steal exclusively from directly connected neighbors to avoid multi-hop communication. Experimental evidence on an HPC cluster with an emulated mesh shows the neighbor-only strategy performs within ~2.2% of global stealing on both balanced and irregular workloads, and an analytical model indicates a growing latency advantage with constellation size. This matters because it demonstrates that neighbor-only stealing can match global stealing performance in emulated settings, suggesting it is a viable and potentially preferable approach for adapting AMT to Space Edge Computing (SEC) at scale.

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Specifying Hardware Communication as Programs

Ernest Ng, Nikil Shyamsunder, Francis Pham, Adrian Sampson 2026-06-14

Interconnect HPC Compiler Runtime Hardware Communication

The problem is that hardware testing requires separate driver and monitor programs for each protocol, leading to manual effort and inconsistency risks. The method proposes a DSL that specifies hardware communication protocols as succinct imperative programs, enabling a single specification to both drive and monitor transactions. The abstract does not disclose experimental results, but describes a tool that automatically infers transaction-level traces from waveforms using the DSL specification. This matters because it could eliminate redundant code and reduce bugs in hardware verification for protocols like Wishbone and AXI-Stream.

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