Jun 24, 2024 · Batching aims to maximize throughput by buffering and reusing a subset of weights and applying them to all samples in a batch. After a set of weights has been ...
Jun 30, 2024 · The mapping algorithm aims to achieve highest weight reuse and reduced data movements for a given CiM prototype and workload. ... Lowprecision CIM modules are ...
8 days ago · In this study, a simple Siamese model with long short-term memory (LSTM) (SSiamese-LSTM) is proposed to achieve a high accuracy of over 99% with limited ...
Jun 25, 2024 · a, Theoretical energy computation of different layers. The computational complexity calculation follows 20 . b, LIF, ALIF and LSTM internal operation schematic.
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4 days ago · In (Azerbayev et al., 2023) large language models are pretrained to solve text only questions bringing on the full power of heavy-weight LLM, but without taking ...
7 days ago · We present a new efficient OpenCL-based Accelerator for large scale Convolutional Neural Networks called Fast Inference on FPGAs for Convolution Neural Network ...
Jun 22, 2024 · Tiny Machine Learning (TinyML) is an emerging technology proposed by the scientific community for developing autonomous and secure devices that can gather, ...
Jun 27, 2024 · The goal was to analyze the performance of a Gradient Boosting Regression Tree (GBRT) versus deep learning models like LSTM, Temporal Fusion Transformer, DeepAR ...
6 days ago · The ZenRobotics Heavy Picker, recognized as the world's strongest recycling robot, efficiently sorts heavy C&D waste, handling materials such as wood ...
Jun 26, 2024 · ... without squaring the errors. While MAE is less sensitive to outliers, it does not provide as much weight to larger errors compared to MSE. MSE:12NN∑i=1 ...