Recently, researchers at the Massachusetts Institute of Technology have created a specialized chip that boosts the speed of neural network computations by 3 to 7 times while cutting power consumption by as much as 95%. This breakthrough makes it feasible to run complex AI models directly on smartphones, without relying on cloud servers.
Whether it's voice recognition or facial detection, most advancements in artificial intelligence today are driven by neural networks. These systems, made up of countless interconnected "neurons," learn tasks by analyzing vast amounts of data. However, their design often leads to high energy use and large memory requirements, making them unsuitable for portable devices.
Currently, smartphone apps using AI must send data to remote servers for processing, which introduces delays and privacy concerns. MIT’s new chip changes this dynamic. It not only accelerates neural network computations but also dramatically reduces energy usage, enabling real-time AI on mobile and smart home devices.
Avishek Biswas, a graduate student in electrical engineering and computer science at MIT, explains the innovation: “Most chips have separate memory and processing units. During computation, data constantly moves back and forth between these sections, consuming a lot of energy.â€
Biswas and his advisor, Anantha Chandrakasan, explored whether the core operation of neural networks—dot multiplication—could be performed directly in memory, eliminating unnecessary data movement. Their research was presented at the International Solid State Circuits Conference, highlighting a major leap in efficient AI hardware.
Artificial neural networks consist of multiple layers, where each node processes inputs from lower layers and passes results to higher ones. Each connection has a weight that determines its influence on the outcome. Training involves adjusting these weights to improve accuracy.
In traditional computing, dot multiplication requires fetching weights and data from memory, performing the calculation, and storing the result. This process repeats for every node, leading to massive data transfers. The MIT chip simplifies this by performing these operations within memory itself, reducing both time and energy.
Inspired by the brain’s synapses, the chip uses voltage-based computations to multiply and add values efficiently. Instead of moving data between components, it processes multiple nodes simultaneously, significantly improving performance.
One key feature is the use of binary weights (0 or 1), which allows the chip to function like a switch, reducing complexity without sacrificing much accuracy. Experiments showed only a 2-3% difference between the chip’s results and those of a traditional system.
Experts like Dario Gil of IBM see this as a promising step toward more efficient AI deployment in IoT devices. With this technology, complex neural networks could soon run locally on phones, cameras, and smart appliances, offering faster and more secure AI experiences.
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The USB 3.2 specification absorbed all prior 3.x specifications. USB 3.2 identifies three transfer rates – 20Gbps, 10Gbps, and 5Gbps.
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Minor update to hub specification to address increased performance and assure seamless transitions between single and two-lane operation
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