Knowledge catalogue
arXiv:2606.21448v1 Announce Type: new Abstract: As large language models scale, memory bandwidth for key-value caches and retrieval-augmented generation systems becomes a critical bottleneck. While 1-
arXiv:2606.20712v1 Announce Type: new Abstract: Sampling-based Model Predictive Control (SMPC) is a promising strategy for contact-rich robotic manipulation, combining gradient-free optimization with
arXiv:2606.22296v1 Announce Type: new Abstract: Edge Internet of Things (IoT) agents are often constrained by memory capacity, privacy requirements, communication latency, and recurring inference cost
This article likely explores the hidden operational processes and internal workings of computers that users typically don't observe, such as microprocessor functions, system-level operations, or the b
arXiv:2512.22219v2 Announce Type: replace-cross Abstract: We introduce Mirage Persistent Kernel (MPK), the first compiler and runtime system that automatically transforms multi-GPU model inference int
arXiv:2606.09516v1 Announce Type: new Abstract: Real-time video restoration (VR) for live streams requires high-resolution outputs under strict per-frame latency constraints. Existing one-step diffusi
arXiv:2606.09312v1 Announce Type: new Abstract: Tensor program optimization is essential for modern machine learning systems, but its search space is enormous. Existing auto-schedulers reduce measurem
arXiv:2601.16622v2 Announce Type: replace-cross Abstract: Equivariant Graph Neural Networks (EGNNs) have become a widely used approach for modeling 3D atomistic systems. However, mainstream architectu
arXiv:2606.06159v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) have the potential to emerge as the third generation of neural networks and have attracted increasing attention across
arXiv:2606.05687v1 Announce Type: new Abstract: In humanoid motion control, model predictive control (MPC) offers physically grounded prediction and constraint handling, while reinforcement learning (
arXiv:2606.05186v1 Announce Type: cross Abstract: Budget-constrained micro-pretraining often requires triaging many candidate recipes on a shared accelerator before larger search budgets are spent. We
arXiv:2606.06130v1 Announce Type: new Abstract: As underwater robotics research increasingly addresses complex 3D perception and autonomous navigation, the fidelity of sonar simulation has become a ke
arXiv:2606.02781v1 Announce Type: cross Abstract: Deep neural networks (DNNs) have achieved state-of-the-art performance across diverse domains. However, typical Von Neumann compute paradigms face sev
Jeremy Howard announced the release of Vui, an open-source voice mode text-to-speech (TTS) model with 300 million parameters that can run on consumer GPUs and Apple Silicon. The model features context
Whether you use it for data preparation, real-time interactive queries, AI model training, or something entirely different, running Apache Spark at scale is demanding — you shouldn’t have to manage th
arXiv:2606.01774v1 Announce Type: cross Abstract: Autoregressive (AR) large language models (LLMs) have achieved broad practical success, but sequential decoding remains a key bottleneck for low-laten
arXiv:2606.00516v1 Announce Type: new Abstract: Mixed batching (MB)--interleaving prefill and decode in a single batch--has become the standard scheduling strategy for large language model (LLM) infer
arXiv:2603.09221v2 Announce Type: replace Abstract: Associative memory has long underpinned the design of sequential models. Beyond recall, humans reason by projecting future states and selecting goal
arXiv:2605.31500v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) are bottlenecked by sparse, irregular memory access. Popular frameworks such as DGL and PyTorch Geometric support general
arXiv:2605.30358v1 Announce Type: new Abstract: Quantum computing remains in the Noisy Intermediate-Scale Quantum (NISQ) era, where the performance is highly constrained to noise. Addressing the limit
When we design large GPU clusters, the network is no longer a background system. It's part of the compute envelope. At the 800G and NVIDIA GB300 NVL72 scale, the back-end fabric accounts for 86% of ne
Together AI developed an optimized speech-to-text system focused on achieving the fastest processing speeds through technical innovations in their inference stack and model optimization. The approach
arXiv:2605.30075v1 Announce Type: new Abstract: Quantum Federated Learning (QFL) offers a promising framework to train quantum models across distributed clients while keeping data strictly local. Due
arXiv:2605.30345v1 Announce Type: new Abstract: Printed circuit board (PCB) schematic design defines nearly all electronic hardware, but it remains manual and expertise-intensive. While generative AI
This Reddit discussion in the StableDiffusion community addresses recommendations for affordable, used or refurbished laptops equipped with GPUs suitable for running open-source image generation model
arXiv:2605.27472v1 Announce Type: cross Abstract: Assertion-based verification (ABV) is a cornerstone of modern hardware design, yet manually translating design intent into formal SystemVerilog Assert
NVIDIA has released GLM-5.1-NVFP4, a quantized version of the GLM-5.1 model, now available on Hugging Face. The model appears to use NVFP4 (NVIDIA's floating-point 4-bit) quantization format, designed
arXiv:2605.25346v1 Announce Type: cross Abstract: Neural network (NN) dynamics models and control policies achieve strong performance in robotics, but providing sound guarantees under uncertainty rema
arXiv:2605.24016v1 Announce Type: cross Abstract: Diffusion inference remains costly for edge deployment, yet existing accelerators focus almost exclusively on score networks because standard drift is
arXiv:2605.22840v1 Announce Type: cross Abstract: How much thinking can a civilisation do? Kardashev's (1964) typology ranks civilisations by total power: planetary (Type I, ~10^16 W), stellar (Type I
arXiv:2605.19255v1 Announce Type: new Abstract: Existing bilateral teleoperation platforms still rely on costly rigid six-axis force/torque sensors, tightly coupled leader-follower hardware, and kiloh
arXiv:2605.19269v1 Announce Type: new Abstract: Transformer training systems are built around dense linear algebra, yet a nontrivial fraction of end-to-end time is spent on surrounding memory-bound op
arXiv:2603.09473v2 Announce Type: replace Abstract: Equipping robotic systems with the capacity to generate extit{ex novo} hardware during operation extends control of physical adaptability. Unlike mo
arXiv:2605.18889v1 Announce Type: cross Abstract: Modern machine learning forces practitioners to choose between powerful but expensive deep networks and fast but limited classical algorithms. Here we
arXiv:2605.17164v1 Announce Type: cross Abstract: Deploying large-scale LLM training and inference with optimal performance is exceptionally challenging due to a complex design space of parallelism st
arXiv:2602.06694v2 Announce Type: replace Abstract: Weight-only quantization has become a standard approach for efficiently serving large language models (LLMs). However, existing methods fail to effi
arXiv:2605.17991v1 Announce Type: cross Abstract: Stable Audio 3 is a family of fast latent diffusion models (small, medium, large) for variable-length audio generation and editing. Since our models c
arXiv:2605.15237v1 Announce Type: cross Abstract: Accelerating applications through the design of hardware accelerators can significantly enhance system performance and energy efficiency. Despite adva
arXiv:2605.14624v1 Announce Type: cross Abstract: A common critique of neural combinatorial-optimization solvers is that they are less energy-efficient than CPU metaheuristics, given the operational e
arXiv:2605.14665v1 Announce Type: new Abstract: Legal reasoning is not semantic similarity search. A court judgment encodes constrained symbolic reasoning: precedent propagation, procedural state tran
arXiv:2506.04499v2 Announce Type: replace Abstract: Existing LiDAR 3D object detection methods predominantely rely on sparse convolutions and/or transformers, which can be challenging to run on resour
arXiv:2605.13925v1 Announce Type: new Abstract: Robotic dexterous hands are central to contact-rich manipulation, with rapid progress driven by advances in hardware, sensing, control, simulation, and
arXiv:2605.14844v1 Announce Type: cross Abstract: We introduce XFP, a dynamic weight quantizer for LLM inference that inverts the conventional workflow: the operator specifies reconstruction quality f
arXiv:2509.19929v4 Announce Type: replace-cross Abstract: Uncertainty Quantification (UQ) is paramount for inference in engineering. A common inference task is to recover full-field information of phy
Cerebras, a company specializing in AI accelerators and wafer-scale computing systems, is discussed in terms of its approaches to improving token generation speed in large language models, which is cr
arXiv:2605.11333v1 Announce Type: cross Abstract: The fast pace of artificial intelligence~(AI) innovation demands an agile methodology for observation, reproduction and optimization of distributed ma
arXiv:2605.10457v1 Announce Type: cross Abstract: Real-time Light Detection And Ranging (LiDAR) simulation must find, per emitted ray, the closest intersecting triangle even in dynamic scenes containi
arXiv:2605.10886v1 Announce Type: cross Abstract: Recent GPU generations deliver significantly higher FLOPs using lower-precision arithmetic, such as FP8. While successfully applied to large language
arXiv:2605.08913v1 Announce Type: cross Abstract: Autoregressive inference is typically assumed to scale predictably with decoding length, and key-value (KV) caching is widely regarded as a universall
arXiv:2603.29002v2 Announce Type: replace-cross Abstract: Modern large language models (LLMs) increasingly depends on efficient long-context processing and generation mechanisms, including sparse atte
This post likely discusses lesser-known Taiwanese companies in the kitchen or consumer appliance sector that play significant roles in powering AI infrastructure, drawing a parallel to how established
arXiv:2603.02883v3 Announce Type: replace Abstract: Diffusion Transformers (DiTs) achieve state-of-the-art video generation quality, but their substantial memory and computational footprints hinder ed
arXiv:2605.06763v1 Announce Type: new Abstract: Sparse attention improves LLM inference efficiency by selecting a subset of key-value entries, but at the cost of potential accuracy degradation. In par
arXiv:2509.05276v4 Announce Type: replace-cross Abstract: Mainstream Transformer-based large language models face major efficiency bottlenecks: training computation scales quadratically with sequence
arXiv:2605.06927v1 Announce Type: cross Abstract: Object detection on heterogeneous edge devices must satisfy strict energy, latency, and memory constraints while still providing reliable perception f
Great collab with @SakanaAILabs on an #ICML26 paper about sparse transformer kernels + formats optimized for modern NVIDIA GPU execution. • TwELL sparse packing • Fused CUDA kernels • 20%+ inference/t
We’ve rolled out a significant update to Google Kubernetes Engine (GKE) that solves one of the most annoying problems in cloud infrastructure: cold start latency. GKE now has up to 4x faster node star
This article discusses optimization techniques for maximizing efficiency on NVIDIA's GB200 NVL72 system using Slurm block scheduling, a job scheduling approach designed to improve resource utilization
arXiv:2605.04357v1 Announce Type: cross Abstract: The usage of large language models (LLMs) has grown increasingly fragmented, with no single model dominating. Meanwhile, cloud providers offer a wide
arXiv:2605.05049v1 Announce Type: cross Abstract: Frontier models increasingly adopt Mixture-of-Experts (MoE) architectures to achieve large-model performance at reduced cost. However, training MoE mo