Are you keeping up with our 11 Days of Inference Acceleration Techniques? Here's the next one:

🤖 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞 𝐝𝐞𝐞𝐩 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐦𝐨𝐝𝐞𝐥 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐧𝐞𝐮𝐫𝐚𝐥 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐬𝐞𝐚𝐫𝐜𝐡 (𝐍𝐀𝐒).

NAS is a technique that…

☝️ From tips 1-5, there’s an assumption that the model is fixed, but it gets even more complicated when you can change the model. 

On Day 6, we’re exploring model level optimizations, which include any method that changes the model or architecture itself. Since neural networks…

We’re at day 5 of actionable inference acceleration techniques for your deep learning applications.

𝐓𝐨𝐝𝐚𝐲, 𝐰𝐞’𝐫𝐞 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐮𝐬𝐞 𝐨𝐟 𝐪𝐮𝐚𝐧𝐭𝐢𝐳𝐚𝐭𝐢𝐨𝐧. 🏋️

Quantization refers to the process of reducing the numerical representation…

It’s day 4 of our 11 Days of Inference Acceleration Techniques. Today, we’re moving on to runtime level optimization best practices.

𝐅𝐨𝐮𝐫𝐭𝐡 𝐭𝐢𝐩: 𝐓𝐚𝐤𝐞 𝐚𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞 𝐨𝐟 𝐠𝐫𝐚𝐩𝐡 𝐜𝐨𝐦𝐩𝐢𝐥𝐚𝐭𝐢𝐨𝐧 📈

Graph compilers such as TVM, Tensor-RT, and OpenVino…

Our third inference acceleration technique recommendation is pretty straightforward:

⚙️ 𝐂𝐨𝐧𝐬𝐢𝐝𝐞𝐫 𝐚𝐝𝐨𝐩𝐭𝐢𝐧𝐠 𝐚 𝐡𝐚𝐫𝐝𝐰𝐚𝐫𝐞 𝐚𝐧𝐝 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐚𝐰𝐚𝐫𝐞 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐭𝐨 𝐦𝐨𝐝𝐞𝐥 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭.

Production-aware model development takes…

It’s day 2 of our 11 Days of Inference Acceleration Techniques. Today, we’re sharing another tip related to hardware.

⚙️ 𝐊𝐧𝐨𝐰 𝐚𝐧𝐝 𝐠𝐞𝐭 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐨𝐮𝐭 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐡𝐚𝐫𝐝𝐰𝐚𝐫𝐞.

In situations where the inference hardware is already defined, you have to make…

📊 Inference is critical to the entire user experience, especially for applications requiring strict performance metrics such as autonomous vehicles and healthcare.

The scale of inference is usually much larger than that of training, and the demand for cost-efficient compute…

📈  #LLM evaluation methods fall into two main categories: automatic and human-in-the-loop.

In this guide, discover the pros and cons of four LLM evaluation methods, equipping you to choose the right evaluation strategy for your LLM needs.

👉 Read now > deci.ai/blog/llm-evalu…

✨ Discover how advanced Qualcomm SNPE quantization techniques help maintain FP32 accuracy while improving latency for YOLO object detection models on Qualcomm Snapdragon devices.

Read now > deci.ai/blog/qualcomm-…

#deeplearning #computervision #neuralnetworks #yolo

🔎 YOLO-NAS-Sat is part of a broader family of highly efficient foundation models, including YOLO-NAS for standard object detection, YOLO-NAS Pose for pose estimation, and DeciSegs for semantic segmentation, all generated through AutoNAC.

Learn more > deci.ai/computer-visio…

MoE, Frankenmerging, SLERP, and Task Vector Algorithms are methods for language model merging, which is a cost-effective way to combine useful LLM skills, usually without the need for fine-tuning.

Here’s an overview that outlines the key characteristics of each model merging…

Learn how a new and tailored image classification model not only maintained baseline accuracy but also boosted throughput by 3X, enhancing the overall efficiency of this manufacturing company’s solution. ⬇️

deci.ai/resources/appl…

#computervision #deeplearning #neuralnetworks

🐉 On the hunt for a Qualcomm Snapdragon NPE quantization recipe for YOLO models?

Hear from Lior Kadoch, Algo Tech Lead at Deci AI, the results of integrating optimized techniques, looking at the contribution of each method's impact on accuracy and overall performance.

For a…