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Found 7 papers, 1 papers with code
A 65 nm Bayesian Neural Network Accelerator with 360 fJ/Sample In-Word GRNG for AI Uncertainty Estimation
Uncertainty estimation is an indispensable capability for AI-enabled, safety-critical applications, e. g. autonomous vehicles or medical diagnosis.
A Remedy to Compute-in-Memory with Dynamic Random Access Memory: 1FeFET-1C Technology for Neuro-Symbolic AI
To address these challenges-and mitigate the typical data-transfer bottleneck of classical Von Neumann architectures-we propose a ferroelectric charge-domain compute-in-memory (CiM) array as the foundational processing element for neuro-symbolic AI.
Efficient approximation of Earth Mover's Distance Based on Nearest Neighbor Search
Earth Mover's Distance (EMD) is an important similarity measure between two distributions, used in computer vision and many other application domains.
In-Memory Nearest Neighbor Search with FeFET Multi-Bit Content-Addressable Memories
Moreover, this approach achieves accuracies comparable to floating-point precision implementations in software for NN classification and one/few-shot learning tasks.
Nonvolatile Spintronic Memory Cells for Neural Networks
A new spintronic nonvolatile memory cell analogous to 1T DRAM with non-destructive read is proposed.
Application-level Studies of Cellular Neural Network-based Hardware Accelerators
While it is well-known that CeNNs can be well-suited for spatio-temporal information processing, few (if any) studies have quantified the energy/delay/accuracy of a CeNN-friendly algorithm and compared the CeNN-based approach to the best von Neumann algorithm at the application level.
A mixed signal architecture for convolutional neural networks
Deep neural network (DNN) accelerators with improved energy and delay are desirable for meeting the requirements of hardware targeted for IoT and edge computing systems.
