ofa elastic, ofa, neural architecture search
Once-for-All elastic kernels support variable kernel sizes within single supernet enabling fine-grained architecture specialization.
9,967 technical terms and definitions
Once-for-All elastic kernels support variable kernel sizes within single supernet enabling fine-grained architecture specialization.
Learn from fixed dataset without interaction.
Adjust recipe to compensate for chamber differences.
Online Hard Example Mining selects high-loss examples within mini-batches for gradient computation improving training efficiency.
Low-resistance contact.
Automated ceiling-mounted system to move FOUPs between tools.
Control overhead transport.
Oil analysis examines lubricant condition and contamination indicating wear and degradation.
Ollama runs local LLMs easily. Pull and run. Mac, Windows, Linux.
Configuration system for complex projects.
Schedule for responding to production issues.
Measure degradation in-situ.
Account for within-die parameter variation.
Run models locally on user devices.
On-device models run locally on user hardware without cloud connectivity.
Measure overlay on product patterns.
Train models directly on edge devices.
On-die decoupling capacitor sizing balances area overhead with impedance targets at high frequencies.
On-die decoupling capacitors integrated in chip area provide fast response to local current demands minimizing dynamic voltage drop.
Measure temperature voltage process on chip.
On-site solar photovoltaic systems generate renewable electricity directly at manufacturing facilities.
Generate augmented data during training.
Once-for-all trains single supernet containing all subnets. Extract subnet for target constraint. Efficient NAS.
Train supernet once search many times.
Once-for-all networks train a single supernet that supports diverse architectural configurations enabling efficient deployment-specific specialization.
One-class SVM for time series learns boundaries of normal behavior detecting anomalies as deviations from learned support.
One-piece flow processes single units through operations without batching enabling rapid response.
One-point lessons teach single concepts on single page for quick learning.
Learn from single example per class.
Single training run to evaluate architectures.
Single example in prompt.
One-shot prompting includes single example demonstrating desired response format.
One-shot pruning removes all unnecessary parameters in single step before retraining.
Prune once without retraining.
One-shot weight sharing trains single supernet where subnetworks share weights for efficient architecture evaluation.
Lower or upper bound only.
One-way ANOVA compares means for single factor with multiple levels.
Intel's unified programming model for diverse architectures.
oneAPI is Intel cross-architecture programming. SYCL-based. Targets CPUs, GPUs, FPGAs with single code.
Distill during training of teacher.
Mine hard examples during training.
Update model as new data arrives.
Online learning updates model continuously as new data arrives. Important for non-stationary environments.
Format for exchanging models between frameworks.
Export to ONNX for portability. Run on different runtimes.
ONNX provides open standard for representing deep learning models enabling framework interoperability.
ONNX Runtime provides cross-platform inference optimization for ONNX format models.
ONNX is an open format for ML models. Export PyTorch/TensorFlow to ONNX for deployment on different runtimes.
ONNX is open model format. Export from PyTorch/TensorFlow, run anywhere. Enables portability.
When you hit GPU OOM, I will help you reduce memory usage by adjusting batch size, sequence length, model size, or using quantization.