Best AI tools for< Research Accelerators >

Hailo is a leading provider of top-performing edge AI processors for various edge devices, offering generative AI accelerators, AI vision processors, and AI accelerators. The company's technology enables high-performance deep learning applications on edge devices, catering to industries such as automotive, security, industrial automation, retail, and personal computing.

The Intel Gaudi AI accelerator developer website provides resources, guidance, tools, and support for building, migrating, and optimizing AI models. It offers software, model references, libraries, containers, and tools for training and deploying Generative AI and Large Language Models. The site focuses on the Intel Gaudi accelerators, including tutorials, documentation, and support for developers to enhance AI model performance.

Rebellions is an AI technology company specializing in AI chips and systems-on-chip for various applications. They focus on energy-efficient solutions and have secured significant investments to drive innovation in the field of Generative AI. Rebellions aims to reshape the future by providing versatile and efficient AI computing solutions.

Move 37 is an AI product studio that has been designing, building, and shipping AI products since 2017. They specialize in designing, developing, and managing AI products, tools, and workflows to help users unlock new levels of insight, productivity, and efficiency. By reimagining how work gets done collaboratively, they aim to bring teams, executives, and organizations together to realize better work futures and products. Move 37 offers services such as generative reporting, research and insights, product development, customer service, and content generation.

AIPodNav is an AI-powered tool designed to enhance your podcast listening experience by providing features such as mind maps, summaries, takeaways, keywords, chapters, and transcriptions. It accelerates knowledge acquisition by 10 times faster than traditional podcast listening methods. AIPodNav aims to revolutionize how users engage with podcasts by offering innovative AI-driven functionalities.

Domino Data Lab is an enterprise AI platform that enables data scientists and IT leaders to build, deploy, and manage AI models at scale. It provides a unified platform for accessing data, tools, compute, models, and projects across any environment. Domino also fosters collaboration, establishes best practices, and tracks models in production to accelerate and scale AI while ensuring governance and reducing costs.

Insight is an AI-powered medical research tool that serves as a research assistant for generating scientific summaries, hypotheses, experimental designs, and target identification. It empowers scientists to navigate literature, formulate hypotheses, and design experiments by utilizing peer-reviewed databases to provide reliable outputs. With integrated features like NIH PubMed access, NIH Reporter insights, and MYGENE & MYVARIANT deep dives, Insight streamlines the research process and accelerates discoveries in the medical field.

Zapata AI is an Industrial Generative AI application that empowers enterprises to revolutionize their industry by building and deploying cutting-edge AI applications. It specializes in tackling complex business challenges with precision using quantum techniques and advanced computing technologies. The platform offers solutions for various industries, accelerates quantum research, and provides expert perspectives on Generative AI and quantum computing.

Paper Pilot is the ultimate AI tool for concise research paper summaries, key insights, and audio guides. It uses cutting-edge AI to enhance research by providing quick, precise summaries of research papers, organizing research boards, and offering an interactive chat for AI-specific questions. Trusted by top researchers, Paper Pilot simplifies and accelerates the study process, saving valuable time and effort.

WEVO is an AI-powered platform that offers effortless UX research for teams. It provides instant and deep insights into digital experiences, helping businesses test, validate, and perfect their online interactions before going live. By combining AI speed with human insight, WEVO boosts creative confidence, accelerates speed to market, and lowers reputational risks. The platform enables users to analyze user behavior, optimize content, measure campaign effectiveness, and enhance customer experiences through data-driven decisions.

Raijin.ai is an AI-powered Customer Discovery and Intelligence Hub designed to help teams aggregate and extract key insights from customer conversations. It accelerates product development by prioritizing features based on customer feedback. The platform offers features like AI Thematic Analysis, Report Writing, Segmentation, and Tags to streamline qualitative research and analysis processes. Raijin.ai is ideal for user researchers, product analysts, and teams looking to integrate AI seamlessly into their workflow to create customer-centric products and data-driven marketing strategies.

Researcher.Life is a comprehensive research support platform that provides AI-powered tools and expert publication services to empower researchers at every stage of their journey. With a suite of advanced AI tools, including Paperpal, R Discovery, and Mind the Graph, Researcher.Life helps researchers write better, discover relevant literature, create stunning scientific illustrations, and find the right journals for their work. Additionally, Researcher.Life offers expert publication services from Editage, ensuring that manuscripts are polished and ready for publication. By combining AI technology with human expertise, Researcher.Life simplifies complex research tasks, saves time, and accelerates the path to success for researchers worldwide.

Trends Critical is an AI Text Generation SaaS application that combines trends with AI to provide faster and better outcomes. It helps users discover trend-validated opportunities, incorporate hype trends into business and personal growth, and create trend-inspired insights with AI. The platform accelerates growth by offering trend-backing partners and real-world hype trends, allowing users to launch products in their country and build partnerships fast. With support for over 50 languages, Trends Critical unlocks 200+ trends and 60+ AI templates, making it a valuable tool for businesses and individuals seeking to stay ahead of the curve.

Super AI is a generative AI tool designed as a copilot for data analysts. It is trained by top-tier product company experts and domain experts to provide unparalleled expertise in research, visualization, and data delivery. The tool goes beyond data processing by generating a comprehensive Business Decision Canvas tailored to specific challenges. Super AI offers guided insights, data modeling suggestions, and effortless integration with legacy BI systems. It is designed to convert business requirements into concrete objectives and is supported by a team of domain experts to mentor the AI. With applications in various industries, Super AI accelerates the process of finding business KPIs and generating data stories with expert intelligence.

Embedl is an AI tool that specializes in developing advanced solutions for efficient AI deployment in embedded systems. With a focus on deep learning optimization, Embedl offers a cost-effective solution that reduces energy consumption and accelerates product development cycles. The platform caters to industries such as automotive, aerospace, and IoT, providing cutting-edge AI products that drive innovation and competitive advantage.

Prem is an AI platform that empowers developers and businesses to build and fine-tune generative AI models with ease. It offers a user-friendly development platform for developers to create AI solutions effortlessly. For businesses, Prem provides tailored model fine-tuning and training to meet unique requirements, ensuring data sovereignty and ownership. Trusted by global companies, Prem accelerates the advent of sovereign generative AI by simplifying complex AI tasks and enabling full control over intellectual capital. With a suite of foundational open-source SLMs, Prem supercharges business applications with cutting-edge research and customization options.

HOPPR is a medical-grade platform that accelerates the development of tailored applications to enhance clinical care and optimize workflows. The platform streamlines AI development in medical imaging, prioritizes data security and privacy, and offers diverse datasets across modalities, anatomies, demographics, and geographies. HOPPR empowers faster prototyping, hypothesis testing, and scalable applications for research and medical imaging services, addressing the growing crisis in medical imaging by enabling partners to rapidly develop solutions that improve patient care, enhance operational efficiency, and increase clinician satisfaction.

Google Research is a leading research organization focusing on advancing science and artificial intelligence. They conduct research in various domains such as AI/ML foundations, responsible human-centric technology, science & societal impact, computing paradigms, and algorithms & optimization. Google Research aims to create an environment for diverse research across different time scales and levels of risk, driving advancements in computer science through fundamental and applied research. They publish hundreds of research papers annually, collaborate with the academic community, and work on projects that impact technology used by billions of people worldwide.

Google Research is a team of scientists and engineers working on a wide range of topics in computer science, including artificial intelligence, machine learning, and quantum computing. Our mission is to advance the state of the art in these fields and to develop new technologies that can benefit society. We publish hundreds of research papers each year and collaborate with researchers from around the world. Our work has led to the development of many new products and services, including Google Search, Google Translate, and Google Maps.

The Google Research Blog is a platform for researchers at Google to share their latest work in artificial intelligence, machine learning, and other related fields. The blog covers a wide range of topics, from theoretical research to practical applications. The goal of the blog is to provide a forum for researchers to share their ideas and findings, and to foster collaboration between researchers at Google and around the world.

LMOps is a research initiative focusing on fundamental research and technology for building AI products with foundation models, particularly enabling AI capabilities with Large Language Models (LLMs) and Generative AI models. The project explores various aspects such as prompt optimization, longer context handling, LLM alignment, acceleration of LLMs, LLM customization, and understanding in-context learning. It also includes tools like Promptist for automatic prompt optimization, Structured Prompting for efficient long-sequence prompts consumption, and X-Prompt for extensible prompts beyond natural language. Additionally, LLMA accelerators are developed to speed up LLM inference by referencing and copying text spans from documents. The project aims to advance technologies that facilitate prompting language models and enhance the performance of LLMs in various scenarios.

DataRobot AI Accelerators are code-first workflows to speed up model development, deployment, and time to value using the DataRobot API. The accelerators include approaches for specific business challenges, generative AI, ecosystem integration templates, and advanced ML and API usage. Users can clone the repo, import desired accelerators into notebooks, execute them, learn and modify content to solve their own problems.

Awesome LLM compression research papers and tools to accelerate LLM training and inference.

MaxText is a high-performance, highly scalable, open-source LLM written in pure Python/Jax and targeting Google Cloud TPUs and GPUs for training and inference. MaxText achieves high MFUs and scales from single host to very large clusters while staying simple and "optimization-free" thanks to the power of Jax and the XLA compiler. MaxText aims to be a launching off point for ambitious LLM projects both in research and production. We encourage users to start by experimenting with MaxText out of the box and then fork and modify MaxText to meet their needs.

PyTorch Lightning is a framework for training and deploying AI models. It provides a high-level API that abstracts away the low-level details of PyTorch, making it easier to write and maintain complex models. Lightning also includes a number of features that make it easy to train and deploy models on multiple GPUs or TPUs, and to track and visualize training progress. PyTorch Lightning is used by a wide range of organizations, including Google, Facebook, and Microsoft. It is also used by researchers at top universities around the world. Here are some of the benefits of using PyTorch Lightning: * **Increased productivity:** Lightning's high-level API makes it easy to write and maintain complex models. This can save you time and effort, and allow you to focus on the research or business problem you're trying to solve. * **Improved performance:** Lightning's optimized training loops and data loading pipelines can help you train models faster and with better performance. * **Easier deployment:** Lightning makes it easy to deploy models to a variety of platforms, including the cloud, on-premises servers, and mobile devices. * **Better reproducibility:** Lightning's logging and visualization tools make it easy to track and reproduce training results.

fastRAG is a research framework designed to build and explore efficient retrieval-augmented generative models. It incorporates state-of-the-art Large Language Models (LLMs) and Information Retrieval to empower researchers and developers with a comprehensive tool-set for advancing retrieval augmented generation. The framework is optimized for Intel hardware, customizable, and includes key features such as optimized RAG pipelines, efficient components, and RAG-efficient components like ColBERT and Fusion-in-Decoder (FiD). fastRAG supports various unique components and backends for running LLMs, making it a versatile tool for research and development in the field of retrieval-augmented generation.

nncase is a neural network compiler for AI accelerators that supports multiple inputs and outputs, static memory allocation, operators fusion and optimizations, float and quantized uint8 inference, post quantization from float model with calibration dataset, and flat model with zero copy loading. It can be installed via pip and supports TFLite, Caffe, and ONNX ops. Users can compile nncase from source using Ninja or make. The tool is suitable for tasks like image classification, object detection, image segmentation, pose estimation, and more.

EasyLM is a one-stop solution for pre-training, fine-tuning, evaluating, and serving large language models in JAX/Flax. It simplifies the process by leveraging JAX's pjit functionality to scale up training to multiple TPU/GPU accelerators. Built on top of Huggingface's transformers and datasets, EasyLM offers an easy-to-use and customizable codebase for training large language models without the complexity found in other frameworks. It supports sharding model weights and training data across multiple accelerators, enabling multi-TPU/GPU training on a single host or across multiple hosts on Google Cloud TPU Pods. EasyLM currently supports models like LLaMA, LLaMA 2, and LLaMA 3.

AI System School is a curated list of research in machine learning systems, focusing on ML/DL infra, LLM infra, domain-specific infra, ML/LLM conferences, and general resources. It provides resources such as data processing, training systems, video systems, autoML systems, and more. The repository aims to help users navigate the landscape of AI systems and machine learning infrastructure, offering insights into conferences, surveys, books, videos, courses, and blogs related to the field.

MaxText is a high performance, highly scalable, open-source Large Language Model (LLM) written in pure Python/Jax targeting Google Cloud TPUs and GPUs for training and inference. It aims to be a launching off point for ambitious LLM projects in research and production, supporting TPUs and GPUs, models like Llama2, Mistral, and Gemma. MaxText provides specific instructions for getting started, runtime performance results, comparison to alternatives, and features like stack trace collection, ahead of time compilation for TPUs and GPUs, and automatic upload of logs to Vertex Tensorboard.

Infinity is a high-throughput, low-latency REST API for serving vector embeddings, supporting all sentence-transformer models and frameworks. It is developed under the MIT License and powers inference behind Gradient.ai. The API allows users to deploy models from SentenceTransformers, offers fast inference backends utilizing various accelerators, dynamic batching for efficient processing, correct and tested implementation, and easy-to-use API built on FastAPI with Swagger documentation. Users can embed text, rerank documents, and perform text classification tasks using the tool. Infinity supports various models from Huggingface and provides flexibility in deployment via CLI, Docker, Python API, and cloud services like dstack. The tool is suitable for tasks like embedding, reranking, and text classification.

ABigSurveyOfLLMs is a repository that compiles surveys on Large Language Models (LLMs) to provide a comprehensive overview of the field. It includes surveys on various aspects of LLMs such as transformers, alignment, prompt learning, data management, evaluation, societal issues, safety, misinformation, attributes of LLMs, efficient LLMs, learning methods for LLMs, multimodal LLMs, knowledge-based LLMs, extension of LLMs, LLMs applications, and more. The repository aims to help individuals quickly understand the advancements and challenges in the field of LLMs through a collection of recent surveys and research papers.

Pgx is a collection of GPU/TPU-accelerated parallel game simulators for reinforcement learning (RL). It provides JAX-native game simulators for various games like Backgammon, Chess, Shogi, and Go, offering super fast parallel execution on accelerators and beautiful visualization in SVG format. Pgx focuses on faster implementations while also being sufficiently general, allowing environments to be converted to the AEC API of PettingZoo for running Pgx environments through the PettingZoo API.

This repository contains the source code for a GEMM & deep learning hardware accelerator system used to validate proposed systolic array hardware architectures implementing efficient matrix multiplication algorithms to increase performance-per-area limits of GEMM & AI accelerators. Achieved results include up to 3× faster CNN inference, >2× higher mults/multiplier/clock cycle, and low area with high clock frequency. The system is specialized for inference of non-sparse DNN models with fixed-point/quantized inputs, fully accelerating all DNN layers in hardware, and highly optimizing GEMM acceleration.

Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.

Awesome-LLM-Inference: A curated list of 📙Awesome LLM Inference Papers with Codes, check 📖Contents for more details. This repo is still updated frequently ~ 👨‍💻‍ Welcome to star ⭐️ or submit a PR to this repo!

The ByteIR Project is a ByteDance model compilation solution. ByteIR includes compiler, runtime, and frontends, and provides an end-to-end model compilation solution. Although all ByteIR components (compiler/runtime/frontends) are together to provide an end-to-end solution, and all under the same umbrella of this repository, each component technically can perform independently. The name, ByteIR, comes from a legacy purpose internally. The ByteIR project is NOT an IR spec definition project. Instead, in most scenarios, ByteIR directly uses several upstream MLIR dialects and Google Mhlo. Most of ByteIR compiler passes are compatible with the selected upstream MLIR dialects and Google Mhlo.

Intel® Neural Compressor is an open-source Python library that supports popular model compression techniques such as quantization, pruning (sparsity), distillation, and neural architecture search on mainstream frameworks such as TensorFlow, PyTorch, ONNX Runtime, and MXNet. It provides key features, typical examples, and open collaborations, including support for a wide range of Intel hardware, validation of popular LLMs, and collaboration with cloud marketplaces, software platforms, and open AI ecosystems.

This repository, Awesome Efficient AIGC, collects efficient approaches for AI-generated content (AIGC) to cope with its huge demand for computing resources. It includes efficient Large Language Models (LLMs), Diffusion Models (DMs), and more. The repository is continuously improving and welcomes contributions of works like papers and repositories that are missed by the collection.

KerasHub is a pretrained modeling library that provides Keras 3 implementations of popular model architectures with pretrained checkpoints. It supports text, image, and audio data for generation, classification, and other tasks. Models are compatible with JAX, TensorFlow, and PyTorch, and can be fine-tuned on GPUs and TPUs. KerasHub components are provided as Layer and Model implementations, extending the core Keras API.

Expert in tailored accelerator search for startups.

General Information Gatherer

Explains Arxiv papers with examples, analogies, and direct PDF links.

Your AI research aide for the latest in ML.

I improve marketing effectiveness by designing stunning research-led assets in a flash!

Your go-to source for well-researched information!

Spot emerging trends in the latest social science research ( (also see, just "Research Radar" for all disciplines))

Teaches and analyzes research methods with in-depth, academic insights.

Expert AI research question assistant.

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Designed to Provide Comprehensive Insights from the AI industry from Reputable Sources.

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Research Proposal Assistant Pro is designed to provide tailored assistance in research writing.

Upon uploading a research paper, I provide a concise section wise analysis covering Abstract, Lit Review, Findings, Methodology, and Conclusion. I also critique the work, highlight its strengths, and answer any open questions from my Knowledge base of Open source materials.

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Find and summarize recent papers in biology, chemistry, and biomedical sciences.

Your AI research assistant, for turning a problem into a research, developing research questions, generating plans, analyzing data and improving research workflows for project success

An assistant for all you research needs

Spot emerging trends in the latest STEM research (also see, just "Research Radar" for all disciplines)

Research topic identifier for graduate students, providing detailed, academic-style problem outlines. (e.g. prompt: suggest research topics in renewable energy)