Asset Details
Do you wish to reserve the book?
Experimentally validated memristive memory augmented neural network with efficient hashing and similarity search
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Experimentally validated memristive memory augmented neural network with efficient hashing and similarity search
Do you wish to request the book?
Experimentally validated memristive memory augmented neural network with efficient hashing and similarity search
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Experimentally validated memristive memory augmented neural network with efficient hashing and similarity search
2022
Overview
Lifelong on-device learning is a key challenge for machine intelligence, and this requires learning from few, often single, samples. Memory-augmented neural networks have been proposed to achieve the goal, but the memory module must be stored in off-chip memory, heavily limiting the practical use. In this work, we experimentally validated that all different structures in the memory-augmented neural network can be implemented in a fully integrated memristive crossbar platform with an accuracy that closely matches digital hardware. The successful demonstration is supported by implementing new functions in crossbars, including the crossbar-based content-addressable memory and locality sensitive hashing exploiting the intrinsic stochasticity of memristor devices. Simulations show that such an implementation can be efficiently scaled up for one-shot learning on more complex tasks. The successful demonstration paves the way for practical on-device lifelong learning and opens possibilities for novel attention-based algorithms that were not possible in conventional hardware.
Memory augmented neural network for lifelong on-device learning is bottlenecked by limited bandwidth in conventional hardware. Here, the authors demonstrate its efficient in-memristor realization with a close-software accuracy, supported by hashing and similarity search in crossbars.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
