SpaceEvo is a novel method introduced by Microsoft researchers to automatically create specialized search spaces for efficient INT8 inference on specific hardware platforms. It offers hardware-specific, quantization-friendly neural network models and outperforms manually designed search spaces. This advancement in deep learning has the potential to enhance edge computing solutions and can be adapted for various model architectures.
Microsoft Researchers Introduce SpaceEvo: A Game-Changer for Designing Ultra-Efficient and Quantized Neural Networks for Real-World Devices
In the world of deep learning, developing efficient deep neural network (DNN) models that perform well on different devices is a challenge. Existing methods focus on automating model design for specific hardware setups, but they often overlook optimizing the search space itself.
That’s where SpaceEvo comes in. It’s a novel method created by a research team that automatically creates specialized search spaces tailored for efficient INT8 inference on specific hardware platforms. What makes SpaceEvo unique is its ability to perform this design process automatically, resulting in hardware-specific, quantization-friendly search spaces.
SpaceEvo’s lightweight design makes it practical and cost-effective, requiring only 25 GPU hours to create hardware-specific solutions. This specialized search space enables the exploration of more efficient models with low INT8 latency, consistently outperforming existing alternatives.
The Benefits of SpaceEvo:
- Automatically creates specialized search spaces for efficient INT8 inference on specific hardware platforms
- Practical and cost-effective design, requiring only 25 GPU hours
- Enables the exploration of more efficient models with low INT8 latency
- Consistently outperforms existing alternatives
SpaceEvo takes into account the factors that affect INT8 latency and creates a diverse population of accurate and INT8 latency-friendly architectures. It uses an evolutionary search algorithm, the Q-T score as a metric, redesigned search algorithms, and a block-wise search space quantization scheme.
The two-stage NAS process ensures that candidate models can achieve comparable quantized accuracy without individual fine-tuning or quantization. Extensive experiments on real-world edge devices and ImageNet demonstrate that SpaceEvo outperforms manually designed search spaces, setting new benchmarks for INT8 quantized accuracy-latency tradeoffs.
In conclusion, SpaceEvo represents a significant advancement in the quest for efficient deep-learning models for real-world edge devices. Its automatic design of quantization-friendly search spaces has the potential to enhance the sustainability of edge computing solutions. The researchers plan to adapt these methods for various model architectures, further expanding their role in deep learning model design and efficient deployment.
If you’re interested in evolving your company with AI and staying competitive, consider leveraging SpaceEvo for designing ultra-efficient and quantized neural networks for real-world devices. Reach out to us at hello@itinai.com for AI KPI management advice and connect with our Telegram t.me/itinainews or Twitter @itinaicom for continuous insights into leveraging AI.
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