Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 3 of 3 for “"computing-in-memory"”.
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Spiking Neural Network with Memristive Based Computing-In-Memory Circuits and Architecture
In recent years neuromorphic computing systems have achieved a lot of success due to its ability to process data much faster and using much less power compared to traditional Von Neumann computing architectures. There are two main types of Artificial Neural Networks (ANNs), Feedforward Neural …
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Enabling Energy-Efficient Hybrid CMOS and Embedded Memory Accelerators for Neuromorphic Computing at the Edge
The deployment of deep learning at the edge promises advances in autonomous driving, computer vision, and IoT, but is limited by the inefficiencies of conventional von Neumann architectures. The physical separation of memory and processing creates a performance bottleneck, with high energy and …
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Effects of Hardware Design Choices on Neural Network Accuracy in Analog Inference Accelerators
… deep neural network (DNN) computations by computing in memory. Unfortunately, device and circuit nonidealities in these accelerators, such as noise and quantization, can also lead to low DNN inference accuracy due to computation errors arising from these non-idealities. These errors are …