Tootfinder

Anybody have access to an oscilloscope with a 100baseTX Ethernet decoder on it? Curious how fast commercial protocol decodes are compared to mine.
I'd love benchmarks:
* 2 channels, 10M points, 500 Msps, on P/N
* Simple edge trigger, saturated or nearly saturated link
* Decode to Ethernet frames
How many waveforms per second / seconds per waveform do you get?

Whole-brain Transferable Representations from Large-Scale fMRI Data Improve Task-Evoked Brain Activity Decoding
Yueh-Po Peng, Vincent K. M. Cheung, Li Su
https://arxiv.org/abs/2507.22378

Whole-brain Transferable Representations from Large-Scale fMRI Data Improve Task-Evoked Brain Activity Decoding
A fundamental challenge in neuroscience is to decode mental states from brain activity. While functional magnetic resonance imaging (fMRI) offers a non-invasive approach to capture brain-wide neural dynamics with high spatial precision, decoding from fMRI data -- particularly from task-evoked activity -- remains challenging due to its high dimensionality, low signal-to-noise ratio, and limited within-subject data. Here, we leverage recent advances in computer vision and propose STDA-SwiFT, a tr…

Scientists Decode 1918 Flu Virus Genome From Century-Old Tissue
https://scitechdaily.com/scientists-decode-1918-flu-virus-genome-from-century-old-tissue/

Sandwich: Separating Prefill-Decode Compilation for Efficient CPU LLM Serving
Juntao Zhao, Jiuru Li, Chuan Wu
https://arxiv.org/abs/2507.18454 https://arxi…

Sandwich: Separating Prefill-Decode Compilation for Efficient CPU LLM Serving
Utilizing CPUs to serve large language models (LLMs) is a resource-friendly alternative to GPU serving. Existing CPU-based solutions ignore workload differences between the prefill and the decode phases of LLM inference, applying a static per-NUMA (Non-Uniform Memory Access) node model partition and utilizing vendor libraries for operator-level execution, which is suboptimal. We propose Sandwich, a hardware-centric CPU-based LLM serving engine that uses different execution plans for the prefill…

Asking for no reason whatsoever: Lots of oscilloscopes have protocol decodes for various flavors of Ethernet.
Have you ever encountered one, from any manufacturer, that is able to decode 100mbit ethernet in real time without dropping packets?

Multimodal LLMs for Visualization Reconstruction and Understanding
Can Liu, Chunlin Da, Xiaoxiao Long, Yuxiao Yang, Yu Zhang, Yong Wang
https://arxiv.org/abs/2506.21319

Multimodal LLMs for Visualization Reconstruction and Understanding
Visualizations are crucial for data communication, yet understanding them requires comprehension of both visual elements and their underlying data relationships. Current multimodal large models, while effective in natural image understanding, struggle with visualization due to their inability to decode the data-to-visual mapping rules and extract structured information. To address these challenges, we present a novel dataset and train multimodal visualization LLMs specifically designed for unde…

Will we ever decode the writing system used by the Inca?
Gift article, no paywall.
https://www.theatlantic.com/culture/archiv

Unraveling the Secrets of the Inca Empire
For hundreds of years, Andean people recorded information by tying knots into long cords. Will we ever be able to read them?

Nexus: Taming Throughput-Latency Tradeoff in LLM Serving via Efficient GPU Sharing
Xiaoxiang Shi, Colin Cai, Junjia Du, Zhanda Zhu, Xingda Wei, Zhihao Jia
https://arxiv.org/abs/2507.06608

Nexus: Taming Throughput-Latency Tradeoff in LLM Serving via Efficient GPU Sharing
Current prefill-decode (PD) disaggregation is typically deployed at the level of entire serving engines, assigning separate GPUs to handle prefill and decode phases. While effective at reducing latency, this approach demands more hardware. To improve GPU utilization, Chunked Prefill mixes prefill and decode requests within the same batch, but introduces phase interference between prefill and decode. While existing PD disaggregation solutions separate the phases across GPUs, we ask: can the sa…

Differential Transformer-driven 6G Physical Layer for Collaborative Perception Enhancement
Soheyb Ribouh, Osama Saleem, Mohamed Ababsa
https://arxiv.org/abs/2506.20597

Differential Transformer-driven 6G Physical Layer for Collaborative Perception Enhancement
The emergence of 6G wireless networks promises to revolutionize vehicular communications by enabling ultra-reliable, low-latency, and high-capacity data exchange. In this context, collaborative perception techniques, where multiple vehicles or infrastructure nodes cooperate to jointly receive and decode transmitted signals, aim to enhance reliability and spectral efficiency for Connected Autonomous Vehicle (CAV) applications. In this paper, we propose an end-to-end wireless neural receiver base…

MNN-AECS: Energy Optimization for LLM Decoding on Mobile Devices via Adaptive Core Selection
Zhengxiang Huang, Chaoyue Niu, Zhaode Wang, Jiarui Xue, Hanming Zhang, Yugang Wang, Zewei Xin, Xiaotang Jiang, Chengfei Lv, Fan Wu, Guihai Chen
https://arxiv.org/abs/2506.19884

MNN-AECS: Energy Optimization for LLM Decoding on Mobile Devices via Adaptive Core Selection
As the demand for on-device Large Language Model (LLM) inference grows, energy efficiency has become a major concern, especially for battery-limited mobile devices. Our analysis shows that the memory-bound LLM decode phase dominates energy use, and yet most existing works focus on accelerating the prefill phase, neglecting energy concerns. We introduce Adaptive Energy-Centric Core Selection (AECS) and integrate it into MNN to create the energy-efficient version, MNN-AECS, the first engine-level…

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Will we ever decode the writing system used by the Inca?
Gift article, no paywall.
https://www.theatlantic.com/culture/archiv

Unraveling the Secrets of the Inca Empire
For hundreds of years, Andean people recorded information by tying knots into long cords. Will we ever be able to read them?

from my link log —
Distance-based ISA for efficient register renaming.
https://www.sigarch.org/distance-based-isa-for-efficient-register-management/
saved 2025-06-04

Distance-Based ISA for Efficient Register Management
CPU cores have become significantly wider over the past decade. Ten years ago, the highest-performance CPUs could decode only up to four instructions simultaneously and execute up to eight instruct…

All this brings me back to some text I was writing yesterday for my students, on which I’d appreciate any thoughtful feedback:
❝You can let the computer do the typing for you, but never let it do the thinking for you.
This is doubly true in the current era of AI hype. If the AI optimists are correct (the credible ones, anyway), software development will consist of humans critically evaluating, shaping, and correcting the output of LLMs. If the AI skeptics are correct, then the future will bring mountains of AI slop to decode, disentangle, fix, and/or rewrite. Either way, it is •understanding• and •critically evaluating• code — not merely •generating• it — that will be the truly essential ability. Always has been; will be even more so. •That• is what you are learning here.❞
11/

ChatGPT-4o, Claude 3.7 Sonnet, Gemini 2.0 Flash, Llama 4, and Copilot comparison: Claude was the best overall with the highest consistency and no hallucinations (Geoffrey A. Fowler/Washington Post)
https://www.washingtonpost.com/technology/

Review | 5 AI bots took our tough reading test. One was smartest — and it wasn’t ChatGPT.
We challenged AI helpers to decode legal contracts, simplify medical research, speed-read a novel and make sense of Trump speeches. Some of the AI analysis was impressive — and some was downright dumb.

Probing for Arithmetic Errors in Language Models
Yucheng Sun, Alessandro Stolfo, Mrinmaya Sachan
https://arxiv.org/abs/2507.12379 https://

Probing for Arithmetic Errors in Language Models
We investigate whether internal activations in language models can be used to detect arithmetic errors. Starting with a controlled setting of 3-digit addition, we show that simple probes can accurately decode both the model's predicted output and the correct answer from hidden states, regardless of whether the model's output is correct. Building on this, we train lightweight error detectors that predict model correctness with over 90% accuracy. We then extend our analysis to structured chain-of…

SASH: Decoding Community Structure in Graphs
Allison Beemer, Jessalyn Bolkema
https://arxiv.org/abs/2507.16583 https://arxiv.org/pdf/…

SASH: Decoding Community Structure in Graphs
Detection of communities in a graph entails identifying clusters of densely connected vertices; the area has a variety of important applications and a rich literature. The problem has previously been situated in the realm of error correcting codes by viewing a graph as a noisy version of the assumed underlying communities. In this paper, we introduce an encoding of community structure along with the resulting code's parameters. We then present a novel algorithm, SASH, to decode to estimated com…

EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding
Bruno Aristimunha, Dung Truong, Pierre Guetschel, Seyed Yahya Shirazi, Isabelle Guyon, Alexandre R. Franco, Michael P. Milham, Aviv Dotan, Scott Makeig, Alexandre Gramfort, Jean-Remi King, Marie-Constance Corsi, Pedro A. Vald\'es-Sosa, Amit Majumdar, Alan Evans, Terrence J Sejnowski, Oren Shriki, Sylvain Chevallier, Arnaud Delorme

EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding
Current electroencephalogram (EEG) decoding models are typically trained on small numbers of subjects performing a single task. Here, we introduce a large-scale, code-submission-based competition comprising two challenges. First, the Transfer Challenge asks participants to build and test a model that can zero-shot decode new tasks and new subjects from their EEG data. Second, the Psychopathology factor prediction Challenge asks participants to infer subject measures of mental health from EEG da…

On the error correction of iterative bounded distance decoding of generalized LDPC codes
David Burshtein
https://arxiv.org/abs/2507.12073 https://

On the error correction of iterative bounded distance decoding of generalized LDPC codes
Consider an ensemble of regular generalized LDPC (GLDPC) codes and assume that the same component code is associated with each parity check node. To decode a GLDPC code from the ensemble, we use the bit flipping bounded distance decoding algorithm, which is an extension of the bit flipping algorithm for LDPC codes. Previous work has shown conditions, under which, for a typical code in the ensemble with blocklength sufficiently large, a positive constant fraction of worst case errors can be corr…

Fast Training-free Perceptual Image Compression
Ziran Zhu, Tongda Xu, Minye Huang, Dailan He, Xingtong Ge, Xinjie Zhang, Ling Li, Yan Wang
https://arxiv.org/abs/2506.16102

Fast Training-free Perceptual Image Compression
Training-free perceptual image codec adopt pre-trained unconditional generative model during decoding to avoid training new conditional generative model. However, they heavily rely on diffusion inversion or sample communication, which take 1 min to intractable amount of time to decode a single image. In this paper, we propose a training-free algorithm that improves the perceptual quality of any existing codec with theoretical guarantee. We further propose different implementations for optimal p…

Towards Unified Neural Decoding with Brain Functional Network Modeling
Di Wu, Linghao Bu, Yifei Jia, Lu Cao, Siyuan Li, Siyu Chen, Yueqian Zhou, Sheng Fan, Wenjie Ren, Dengchang Wu, Kang Wang, Yue Zhang, Yuehui Ma, Jie Yang, Mohamad Sawan
https://arxiv.org/abs/2506.12055

Towards Unified Neural Decoding with Brain Functional Network Modeling
Recent achievements in implantable brain-computer interfaces (iBCIs) have demonstrated the potential to decode cognitive and motor behaviors with intracranial brain recordings; however, individual physiological and electrode implantation heterogeneities have constrained current approaches to neural decoding within single individuals, rendering interindividual neural decoding elusive. Here, we present Multi-individual Brain Region-Aggregated Network (MIBRAIN), a neural decoding framework that co…

Is cheese secretly fueling your nightmares? Science weighs in #AnimalRights

Is cheese secretly fueling your nightmares? Science weighs in
Over a thousand students revealed a striking link between lactose intolerance and nightmare-filled nights, hinting that midnight stomach turmoil from dairy can invade dreams. Researchers suggest simple diet tweaks—especially ditching late-night cheese—could turn scary sleep into sweet rest, though more experiments are needed to decode the gut-dream connection.

Check this out! https://andrawatkins.substack.com/?r=jiw37&utm_campaign=referrals-subscribe-page-share-screen&utm_medium=web

For Such a Time as This | Andra Watkins | Substack
A guide to decode the country America has chosen to be. Click to read For Such a Time as This, by Andra Watkins, a Substack publication with tens of thousands of subscribers.

This https://arxiv.org/abs/2501.05460 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Efficiently Serving Large Multimodal Models Using EPD Disaggregation
Large Multimodal Models (LMMs) extend Large Language Models (LLMs) by handling diverse inputs such as images, audio, and video, but at the cost of adding a multimodal encoding stage that increases both computational and memory overhead. This step negatively affects key Service Level Objectives (SLOs), such as time to first token (TTFT) and time per output token (TPOT). We introduce Encode-Prefill-Decode (EPD) Disaggregation, a novel framework that separates the encoding, prefill, and decode sta…

UltrasonicSpheres: Localized, Multi-Channel Sound Spheres Using Off-the-Shelf Speakers and Earables
Michael K\"uttner, Valeria Sitz, Kathrin Gerling, Michael Beigl, Tobias R\"oddiger
https://arxiv.org/abs/2506.02715

UltrasonicSpheres: Localized, Multi-Channel Sound Spheres Using Off-the-Shelf Speakers and Earables
We present a demo ofUltrasonicSpheres, a novel system for location-specific audio delivery using wearable earphones that decode ultrasonic signals into audible sound. Unlike conventional beamforming setups, UltrasonicSpheres relies on single ultrasonic speakers to broadcast localized audio with multiple channels, each encoded on a distinct ultrasonic carrier frequency. Users wearing our acoustically transparent earphones can demodulate their selected stream, such as exhibit narrations in a chos…

SPINT: Spatial Permutation-Invariant Neural Transformer for Consistent Intracortical Motor Decoding
Trung Le, Hao Fang, Jingyuan Li, Tung Nguyen, Lu Mi, Amy Orsborn, Uygar S\"umb\"ul, Eli Shlizerman
https://arxiv.org/abs/2507.08402

SPINT: Spatial Permutation-Invariant Neural Transformer for Consistent Intracortical Motor Decoding
Intracortical Brain-Computer Interfaces (iBCI) aim to decode behavior from neural population activity, enabling individuals with motor impairments to regain motor functions and communication abilities. A key challenge in long-term iBCI is the nonstationarity of neural recordings, where the composition and tuning profiles of the recorded populations are unstable across recording sessions. Existing methods attempt to address this issue by explicit alignment techniques; however, they rely on fixed…

TD-Pipe: Temporally-Disaggregated Pipeline Parallelism Architecture for High-Throughput LLM Inference
Hongbin Zhang, Taosheng Wei, Zhenyi Zheng, Jiangsu Du, Zhiguang Chen, Yutong Lu
https://arxiv.org/abs/2506.10470

TD-Pipe: Temporally-Disaggregated Pipeline Parallelism Architecture for High-Throughput LLM Inference
As the model size continuously increases, pipeline parallelism shows great promise in throughput-oriented LLM inference due to its low demand on communications. However, imbalanced pipeline workloads and complex data dependencies in the prefill and decode phases result in massive pipeline bubbles and further severe performance reduction. To better exploit the pipeline parallelism for high-throughput LLM inference, we propose TD-Pipe, with the key idea lies in the temporally-disaggregated pipeli…

Over-the-Air Fronthaul Signaling for Uplink Cell-Free Massive MIMO Systems
Zakir Hussain Shaik, Sai Subramanyam Thoota, Emil Bj\"ornson, Erik G. Larsson
https://arxiv.org/abs/2506.00655

Over-the-Air Fronthaul Signaling for Uplink Cell-Free Massive MIMO Systems
We propose a novel resource-efficient over-the-air(OTA) computation framework to address the huge fronthaul computational and control overhead requirements in cell-free massive multiple-input multiple-output (MIMO) networks. We show that the global sufficient statistics to decode the data symbols can be computed OTA using the locally available information at the access points (APs). We provide the essential signal processing aspects at the APs and the central processing unit (CPU) to facilitate…

Hardware-Centric Analysis of DeepSeek's Multi-Head Latent Attention
Robin Geens, Marian Verhelst
https://arxiv.org/abs/2506.02523 https://

Hardware-Centric Analysis of DeepSeek's Multi-Head Latent Attention
Multi-Head Latent Attention (MLA), introduced in DeepSeek-V2, improves the efficiency of large language models by projecting query, key, and value tensors into a compact latent space. This architectural change reduces the KV-cache size and significantly lowers memory bandwidth demands, particularly in the autoregressive decode phase. This letter presents the first hardware-centric analysis of MLA, comparing it to conventional Multi-Head Attention (MHA) and evaluating its implications for accele…

This https://arxiv.org/abs/2506.03296 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Parallel CPU-GPU Execution for LLM Inference on Constrained GPUs
Deploying large language models (LLMs) for online inference is often constrained by limited GPU memory, particularly due to the growing KV cache during auto-regressive decoding. Hybrid GPU-CPU execution has emerged as a promising solution by offloading KV cache management and parts of attention computation to the CPU. However, a key bottleneck remains: existing schedulers fail to effectively overlap CPU-offloaded tasks with GPU execution during the latency-critical, bandwidth-bound decode phase…

Parallel CPU-GPU Execution for LLM Inference on Constrained GPUs
Jiakun Fan, Yanglin Zhang, Xiangchen Li, Dimitrios S. Nikolopoulos
https://arxiv.org/abs/2506.03296

Parallel CPU-GPU Execution for LLM Inference on Constrained GPUs
Deploying large language models (LLMs) for online inference is often constrained by limited GPU memory, particularly due to the growing KV cache during auto-regressive decoding. Hybrid GPU-CPU execution has emerged as a promising solution by offloading KV cache management and parts of attention computation to the CPU. However, a key bottleneck remains: existing schedulers fail to effectively overlap CPU-offloaded tasks with GPU execution during the latency-critical, bandwidth-bound decode phase…