{"id":33818,"date":"2023-11-04T03:00:00","date_gmt":"2023-11-04T10:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=33818"},"modified":"2023-11-03T13:28:11","modified_gmt":"2023-11-03T20:28:11","slug":"ibms-groundbreaking-analog-ai-chip-ushers-new-era-of-efficiency-and-accuracy","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2023\/11\/04\/ibms-groundbreaking-analog-ai-chip-ushers-new-era-of-efficiency-and-accuracy\/","title":{"rendered":"IBM\u2019s Groundbreaking Analog AI Chip: Ushers New Era\u00a0 of Efficiency and Accuracy\u00a0\u00a0"},"content":{"rendered":"\n

Introduction\u00a0<\/strong>\u00a0<\/p>\n\n\n\n

The biggest technological leap that we have made in this decade is Artificial intelligence (AI). These AI tools\u00a0 are rapidly transforming the world as we know it today. But currently It\u2019s widespread adoption is limited by\u00a0 the high energy consumption and cost of traditional digital AI chips. To address this issue IBM have made\u00a0 a ground breaking advancement for AI, IBM has unveiled a cutting edge analog AI chip that promises and\u00a0 has potential to redefine the landscape of deep neural networks (DNNs). This chip is 100 times more\u00a0 energy-efficient and up to 10 times faster than traditional digital AI chips for performing deep neural\u00a0 network (DNN) computations.\u00a0<\/p>\n\n\n\n

What is IBM analog AI chip?\u00a0<\/strong>\u00a0<\/p>\n\n\n\n

A traditional digital AI chip differ from analog AI chip in the way both of them process data. A digital AI chip  use binary bit (0s and 1s) to represent data and preform calculations. However, analog AI chip uses  continuous electrical signals to represent data and perform calculations. This way it allows analog AI chip  to process much more efficiently that digital AI chip, especially for complex computations such as used in  DNNs <\/p>\n\n\n\n

IBMs analog AI chip uses variety of modern techniquesto improve the efficiency and accuracy. At the center  of these technique is a transformative technology phase change memory (PCM). PCM is a type of non volatile memory that can retain data even when the power is turned off. This an approach for encoding  network weights directly onto the chip, resulting in high speed and energy efficiency. This storage is  typically very large and need to be accessed very quickly during computation. Another notable technique  they have used is time-based analog-to-digital converters (ADCs). Time-based ADCs are very efficient at  converting the continuous electrical signals used to represent data in analog AI chips to digital signals. <\/p>\n\n\n\n

This new chip comprises of 64 analog in-memory compute cores, a monumental stride in the analog AI  solution, thus removing the burden of data transfer requirements as seen in traditional system by executing <\/p>\n\n\n\n

computation directly within memory. This chip is really good at handling computer vision AI tasks like\u00a0 recognizing thing in pictures, in comparison with its counter digital AI chip all the while conserving more\u00a0 energy. Also, it\u2019s good at understanding natural language (human language) AI tasks but uses less energy\u00a0 almost 14 times less energy.\u00a0<\/p>\n\n\n

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With introduction this chip the future where we can use energy efficient AI is no longer a distant dream.  IBMs analog AI chip is still in the early stages of development but has the potential to completely change  the way we use AI making it more efficient and affordable. IBM research is also working towards integrating  both streams into one chip creating analog mixed signal chip for future. This chip could be used in  developing new AI powered applications in many fields, such as healthcare, finance, education,  transportation. Although, this will play a major significance in the AI systems there is lot of ground work to  complete before we witness these integrations into customer market. <\/p>\n\n\n\n

How IBM\u2019s Analog AI Chip works\u00a0\u00a0<\/strong><\/p>\n\n\n\n

IBM’s analog AI chip make use of various techniques to improve its efficiency and accuracy for performing  DNN (deep neural network) computations. <\/p>\n\n\n\n

Some of the key techniques used by this chip:\u00a0<\/p>\n\n\n\n