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Found 8 papers, 0 papers with code
CiMNet: Towards Joint Optimization for DNN Architecture and Configuration for Compute-In-Memory Hardware
With the recent growth in demand for large-scale deep neural networks, compute in-memory (CiM) has come up as a prominent solution to alleviate bandwidth and on-chip interconnect bottlenecks that constrain Von-Neuman architectures.
SimQ-NAS: Simultaneous Quantization Policy and Neural Architecture Search
In this paper, we demonstrate that by using multi-objective search algorithms paired with lightly trained predictors, we can efficiently search for both the sub-network architecture and the corresponding quantization policy and outperform their respective baselines across different performance objectives such as accuracy, model size, and latency.
InstaTune: Instantaneous Neural Architecture Search During Fine-Tuning
However, training super-networks from scratch can be extremely time consuming and compute intensive especially for large models that rely on a two-stage training process of pre-training and fine-tuning.
A Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalities
Recent advances in Neural Architecture Search (NAS) such as one-shot NAS offer the ability to extract specialized hardware-aware sub-network configurations from a task-specific super-network.
A Hardware-Aware System for Accelerating Deep Neural Network Optimization
These methods decouple the super-network training from the sub-network search and thus decrease the computational burden of specializing to different hardware platforms.
Accelerating Neural Architecture Exploration Across Modalities Using Genetic Algorithms
Neural architecture search (NAS), the study of automating the discovery of optimal deep neural network architectures for tasks in domains such as computer vision and natural language processing, has seen rapid growth in the machine learning research community.
TrimBERT: Tailoring BERT for Trade-offs
Models based on BERT have been extremely successful in solving a variety of natural language processing (NLP) tasks.
Undivided Attention: Are Intermediate Layers Necessary for BERT?
In recent times, BERT-based models have been extremely successful in solving a variety of natural language processing (NLP) tasks such as reading comprehension, natural language inference, sentiment analysis, etc.
