ParityQC’s Post

Meta challenges transformer architecture with Megalodon LLM Megalodon also uses “chunk-wise attention,” which divides the input sequence into fixed-size blocks to reduce the complexity of the model from quadratic to linear.Read More https://ift.tt/uMIftVL https://ift.tt/KMVfw7l

I’d never seen a DL module benchmarked in terms of peak theoretical #GPU flop performance before. Neat. Even better: Hopper architecture tweaks in detail.

Let's explore the concepts at the core of our technology - starting from the Parity Compilation! The paper "Constructive Plaquette Compilation for the Parity Architecture" introduces a constructive compilation algorithm that is based on building a rectangular layout of plaquettes, one layer at a time. The plaquettes represent the constraints among Parity Qubits, which dictate their connectivity and interactions. Learn more below, and read the full paper here: https://lnkd.in/etp49u59 Roeland ter Hoeven Ben Niehoff Sagar Kale Wolfgang Lechner

🔥 #hottopic End-to-End Convolutional Network and Spectral-Spatial Transformer Architecture for Hyperspectral Image Classification by Shiping Li, et al. ➡️ https://brnw.ch/21wLeH8

NOW ON-DEMAND! Watch this introduction to the AMD Versal architecture, which explores the differences between the various families of AMD Versal devices, as well as delving into the different development frameworks and entry points available depending on the target application: https://lnkd.in/e-vWnXat https://lnkd.in/ebuEEGyi

Engineered with a digitally implemented, phase-locked loop architecture, the flexible Moku Phasemeter allows complete characterization of a system by measuring phase ✅, frequency ✅, and amplitude ✅. If you need live frequency domain insights into signal characteristics, it's easy to visualize your data with the power spectral density graph. Need to characterize oscillator stability? Swap to the Allan deviation plot! 📉

NOW ON-DEMAND! Watch this introduction to the AMD Versal architecture, which explores the differences between the various families of AMD Versal devices, as well as delving into the different development frameworks and entry points available depending on the target application: https://lnkd.in/eZyvqJbe https://lnkd.in/ep_hHeuZ

I'm excited to share details about the YOLOv7 object detection architecture! 🚀 I'm incredibly impressed by the ingenious methods proposed in this work! Let me know if you'd like to discuss the paper in more detail. Paper:https://lnkd.in/gxb2V-Uy #YOLOv7 #ObjectDetection #ComputerVision #AIMethods

AnchorGT: A Novel Attention Architecture for Graph Transformers as a Flexible Building Block to Improve the Scalability of a Wide Range of Graph Transformer Models https://buff.ly/3V1Aptp

The paper 'The Llama3 Herd of Models' is fascinating to read, especially as it highlights that it uses a standard dense transformer architecture from the original transformer paper with minor modifications. Llama 3 is a model trained to predict the next token of a sequence. It is pre-trained with 405 B parameters on 15.6 T tokens! It is then post-trained with human feedback. I recommend reading the paper https://lnkd.in/eFVU92W2