TetraMem Inc., a leader in Analog In-Memory Computing (A-IMC) technology, and SK hynix Inc., a global leader in AI memory and semiconductor technologies, today announced the successful completion of a ...
AI chip makers SK hynix and TetraMem unveil a chip that computes inside memory to reduce power use and data movement.
TPUs are Google’s specialized ASICs built exclusively for accelerating tensor-heavy matrix multiplication used in deep learning models. TPUs use vast parallelism and matrix multiply units (MXUs) to ...
Abstract: Sparse Convolutional Neural Network (CNN) training is well known to be time-consuming due to significant off-chip memory traffic. To effectively deploy sparse training, existing accelerators ...
The world of artificial intelligence (AI) is rapidly evolving, and AI is increasingly enabling applications that were previously unattainable or very difficult to implement. A subsequent article, ...
We consider matrix completion for recommender systems from the point of view of link prediction on graphs. Interaction data such as movie ratings can be represented by a bipartite user-item graph with ...
Recent research in dynamic convolution shows substantial performance boost for efficient CNNs, due to the adaptive aggregation of K static convolution kernels.It has two limitations: (a) it increases ...
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