Recogni

Say hello to Pareto, the world’s first GenAI log-math number system to make GenAI inference compute orders of magnitude faster and cheaper. 🔴 AI calculations need a lot of power Modern AI transformer models like Llama, Falcon, or Mistral involve trillions of multiplications every second - and multiplications are exponentially growing as AI becomes more capable. But adding numbers uses a lot fewer silicon transistors and consumes far less power than multiplying them. 🔵 Pareto makes AI calculations more efficient By turning multiplications and divisions into additions and subtractions, Pareto makes GenAI inference compute smaller, faster, and more energy-efficient, all while keeping accuracy extremely high. Extensive testing on various AI models, including Mixtral-8x22B, Llama3-70B, Falcon-180B, Stable Diffusion XL, and Llama3.1-405B shows that Pareto achieves a relative accuracy of over 99.9% compared to the trained high-precision baseline model and consumes significantly less power. 🟢 Pareto makes GenAI inference more profitable >> Pareto helps data centers and enterprises to maximize utilization of compute, space, and energy - drastically lowering total cost of ownership. >> Pareto helps developers to bring new models to production in less time and with higher accuracy - generating revenue more quickly and with better profit margins. In February of this year, we raised $103M Series C funding to double down on our strategic focus and our mission to accelerate the world’s AI ambitions. The round was co-led by Celesta Capital and GreatPoint Ventures. With special thanks to Ashok Krishnamurthi, Sriram Viswanathan, and Kevin Johnson. If you want to know more about Pareto’s nitty gritty details, join us at NeurIPS 2024.

AI Hardware & Edge AI Summit, here we come 🫶 Our CPO, R K Anand will host a panel discussion with top panelists: Gaia Bellone, Alex Pham, and Michael Stewart. If you are at the conference and you want to learn more about Recogni, let us know. We are happy to connect and chat. See you there 👋

In his latest Forbes Technology Council article, Recogni CEO, Marc Bolitho, discusses how next-gen AI chipsets are reshaping energy demands and what this means for our future. Link to the article in the comments 👇 Image credit: Getty

In his latest article for Forbes Technology Council, Recogni CEO Marc Bolitho discusses the critical role of investing in hardware to advance generative AI. As generative AI models become larger and more complex, the need for specialized hardware accelerators and efficient computing infrastructure becomes vital to support training and inference, ultimately driving innovation and enabling new applications and businesses. https://lnkd.in/eihA5PUc

Low power consumption and efficient power processing are key to AV adoption – period.   “In addition to a potential future burden on data storage infrastructure, the AI systems deployed in AVs are generally highly inefficient. Marc Bolitho, Chief Executive at AI company Recogni, tells Automotive World that the repurposed technology providing a foundation for these systems is the root cause. “The automotive industry basically adopted an existing solution that’s used in data centres for training AI networks, a graphics processing unit (GPU). These are great for parallel processing, but the hardware is large and power hungry: a GPU needs energy to function, as well as a liquid cooling system for dissipating heat.” https://lnkd.in/gV-JiMvq Automotive World Will Girling Marc Bolitho #AI #AIinference #powerconsumption #autonomousvehicles #AV #energy #energyefficiency #level2 #level3 #level4 #automotive #OEM #autosuppliers #GPU #datacenter #datacentre #noneedforliquidcooling #trainingAI #processingAI #trainingmodelsisacostcenter #inferenceisaprofitcenter #costvsprofit

Recogni co-founder and CPO, RK Anand, talks to PYMNTS.com: What's Hot in Retail Today senior reporter, Austin Prey, about the importance of power efficiency and cost efficiency when it comes to AI inference. Right now it’s 80% training, 20% inference, but at some point in the future it will flip to be 80% inference – at that point inference MUST be exponentially cheaper and more power efficient. Otherwise enterprises will run out of money and as Elon Musk predicts, we’ll also run out of energy. “Training is an unavoidable cost center,” Anand explained. “You have to spend lots of money to build the models. But inference can be a profit center, and that’s because the elements associated with inference are how much does it cost for me to run that inference system, and how much am I going to charge customers to use it, and is there a differential that results in a profit for me to deliver that service? The economics of inference matter the most.” https://lnkd.in/gvSA4Jh3 #AI #AIprocessing #AItraining #AIinference #elonmusk #energy #energyefficiency #fintech #financialservices #enterprise

We have a power consumption emergency and Recogni is a big part of the solution. “A January 2023 paper from the Massachusetts Institute of Technology (MIT) calculated that the processing power required to support mass adoption (one billion vehicles in the study) of SAE Level 4+ could double data centres’ annual CO2 emissions. MIT’s conclusion was that emissions and energy efficiency should become essential aspects of AV design. To resolve these issues, automakers and suppliers must go back to basics and determine the fundamental inefficiencies of artificial intelligence (AI) in the industry today.” https://lnkd.in/gV-JiMvq Automotive World Massachusetts Institute of Technology Will Girling SAE International #AI #AIinference #powerconsumption #autonomousvehicles #AV #energy #energyefficiency #level2 #level3 #level4 #automotive #OEM #autosuppliers 

Kevin Johnson, former CEO of Starbucks and Juniper Networks, joins Recogni as our newest board member and investor. His leadership experience and expertise in Generative AI enhance our team as we lead in AI inference technology. With Kevin's insights, we're poised to expand AI's accessibility and prioritize sustainability in a pivotal moment of growth. https://lnkd.in/emu4BB_J #Recogni #GenerativeAI #AIInnovation #AIProcessing 

In this Forbes article, Recogni CEO Marc Bolitho discusses the growing energy demands of generative AI technologies and their impact on the environment. He also highlights strategies for managing these demands sustainably while fostering innovation in the AI sector. https://lnkd.in/eRMWksBf #Recogni #GenerativeAI #EnergyEfficiency

Excited to see our CEO, @Marc-Bolitho, quoted in this @Fast-Company article by @HarryMcCracken about Nvidia’s continued leadership in the AI space, while there is also big opportunity for startups like Recogni to tackle very specific problems like inference. “For example, instead of taking on the market for training AI models, where Nvidia hardware might prove impossible to displace, chip startup Recogni is focusing on inference— the process of extracting information from a model and leveraging it for a particular purpose, such as an advanced driver-assistance system. ‘Nvidia is using the same solution for training and inference,” explains Recogni CEO Marc Bolitho. “We are working toward a purpose-built, optimized solution for AI inference, and we see that market growing very rapidly as the user base for generative AI increases.’ Investors see enough potential in this kind of targeted competition that Recogni was able to announce a $102 million funding round in February.” https://bit.ly/4akvKrb #AI #AIchip #inference #generativeAI #visioninference #inferenceprocessing #innovation #AIinnovation