Digital chips have revolutionized the world beyond all recognition. Yet, despite their enormous advantages to computing, for power-hungry processes like AI, analog might be best.
using the mathematical operation of matrix multiplication, which involves multiplying two rectangular arrays of numbers. This occurs when information travels between artificial brain cells or neurons. To make fast decisions, many 'artificial neurons' must simultaneously send data to many other neurons. This is a huge, complicated process. For this reason, graphics processing units , made for handling these fast, big processes, are now widely used in AI development.
When an AI is ready to do its job, it still needs a big GPU with all the power and heat it creates. This also makes it more difficult to put AI into small devices like cameras or robots, where there is little room for all that power and heat. Analog chips can't replace digital ones for everything AI does, especially when working with people or getting info. But combining the best parts of both types of chips could help make AI even better, not just for high-tech stuff but also for smart devices in homes and factories worldwide.Innatera, Rain Neuromorphic,
AI and machine learning primarily rely on digital chips, but there is a place for analog chips on the edge, such as in smartphones and cars, which require instant intelligence without needing to send data to the cloud. Innatera's third-generation AI chip has 256 neurons and 65,000 synapses and runs inference at under one milliwatt. The chip is used by customers to run radar and audio applications, aiming to incorporate low levels of learning and inference on the device.
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