8 citations · 9 across the 12 of their papers we have counts for
4 papers · 1 filter
Direction-Preserving Number Representations
Bardia Zadeh, George A. Constantinides
Low-precision number formats are widely used in modern machine learning systems due to their efficiency. Accurate direction representation is key to the accuracy of vector operatio…
Training with Fewer Bits: Unlocking Edge LLMs Training with Stochastic Rounding
Taowen Liu, Marta Andronic, Deniz Gündüz +1
LLM training is resource-intensive. Quantized training improves computational and memory efficiency but introduces quantization noise, which can hinder convergence and degrade mode…
NeuraLUT-Assemble: Hardware-aware Assembling of Sub-Neural Networks for Efficient LUT Inference
Marta Andronic, George A. Constantinides
Efficient neural networks (NNs) leveraging lookup tables (LUTs) have demonstrated significant potential for emerging AI applications, particularly when deployed on field-programmab…
PolyLUT: Ultra-low Latency Polynomial Inference with Hardware-Aware Structured Pruning
Marta Andronic, Jiawen Li, George A. Constantinides
Standard deep neural network inference involves the computation of interleaved linear maps and nonlinear activation functions. Prior work for ultra-low latency implementations has…