This paper introduces Block Data Representations (BDR), a framework for exploring and
evaluating a wide spectrum of narrow-precision formats for deep learning. It enables …

Monitoring palm tree seedlings and plantlings presents a formidable challenge because of
the microscopic size of these organisms and the absence of distinguishing morphological …

The increasing size of deep neural networks necessitates effective model compression to
improve computational efficiency and reduce their memory footprint. Sparsity and …

Time series forecasting is the problem of predicting future data samples from historical
information and recent deep neural networks (DNNs) based techniques have achieved …

The remarkable advancements in AI algorithms over the past three decades have been
paralleled by an exponential growth in their complexity, with parameter counts soaring from …

Model quantization represents both parameters (weights) and intermediate values
(activations) in a more compact format, thereby directly reducing both computational and …