Chalcogenides? Like Ovonics?[1] That led decades ago to fast-read, slow-write memory devices. It was a dead end in electronics, but it did work. Makes sense for something that is slowly reconfigurable, like an FPGA.
"This advancement could shift information processing from data centers to local devices, providing real-time responses and extending battery life for applications like robotic motion control and autonomous vehicles."
> The TPU is constructed with a systolic array architecture that allows parallel 2 bit integer multiply–accumulate operations. A five-layer convolutional neural network based on the TPU can perform MNIST image recognition with an accuracy of up to 88% for a power consumption of 295 µW. We use an optimized nanotube fabrication process [...] 1 TOPS/W/s
Chalcogenides? Like Ovonics?[1] That led decades ago to fast-read, slow-write memory devices. It was a dead end in electronics, but it did work. Makes sense for something that is slowly reconfigurable, like an FPGA.
"This advancement could shift information processing from data centers to local devices, providing real-time responses and extending battery life for applications like robotic motion control and autonomous vehicles."
Now that's a big stretch.
[1] https://en.wikipedia.org/wiki/Energy_Conversion_Devices
I've heard it said that, we could do quantum computational operations with analog electronic components except for the variance in component quality.
How do these fuzzy logic electronic components overcome the same challenges as analog electronic quantum computing?
The article describes performance on a visual CNN Convolutional Neural Network ML task.
Is quantum logic the only sufficient logic to describe systems with phase?
What is most production cost and operating cost efficient at this or similar ML tasks?
For reference, from https://news.ycombinator.com/item?id=41322088 re: "A carbon-nanotube-based tensor processing unit" (2024) https://www.nature.com/articles/s41928-024-01211-2 :
> The TPU is constructed with a systolic array architecture that allows parallel 2 bit integer multiply–accumulate operations. A five-layer convolutional neural network based on the TPU can perform MNIST image recognition with an accuracy of up to 88% for a power consumption of 295 µW. We use an optimized nanotube fabrication process [...] 1 TOPS/W/s
ScholarlyArticle: "A van der Waals interfacial junction transistor for reconfigurable fuzzy logic hardware" (2024) https://www.nature.com/articles/s41928-024-01256-3