The Ultimate Inference Acceleration Guide

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- About the Guide

The Challenges of Deep Learning Inference

With deep learning models becoming more prevalent not only in academia and research but also in industry applications, there is a need to focus not only on training and reaching the desired accuracy but also on overall inference performance and cost efficiency.

In this guide, you’ll gain a deeper understanding of deep learning inference, explore different ways to improve inference performance, learn from various industry use cases, and discover actionable tips to apply to your existing applications. 

Explore the outline below to glimpse the contents of our guide. For a complete understanding and to access all the lessons, download the full guide.

Fast and efficient inference is central to powerful deep-learning applications. It is critical to user experience and has significant cost reduction implications. Today, there are five primary challenges that AI teams face when trying to get their models to run fast and efficient inference in production:

  • Real-time Performance: Efficient models are crucial for optimal user experience and safety, especially in applications like autonomous cars.
  • Compute cost – inefficient inference and hardware underutilization escalate compute costs for scaling deep learning products.
  • Resource-Constrained Devices: Challenges include limited memory and processing power, which make it difficult to run complex deep learning models efficiently.
  • Multi-Hardware Deployment: Models often need to be optimized for diverse devices, but one size model doesn’t fit all hardware.
  • Engineering Hurdles: Transitioning from training to deployment involves model conversion, hardware selection, continuous deployment, and constructing intricate inference pipelines.

Common Inference Acceleration Techniques

Different components play a role in inference acceleration, and optimizing each of them can boost the inference performance of deep learning-based applications.

The inference acceleration stack comprises various layers – from the selected deep neural architecture used to the hardware selected for inference.  All of these components are inextricably linked and changes to any of these layers can accelerate the inference process. By wisely improving several layers together, you can achieve a significant speed-up.

Aligning model architecture with inference hardware

Hardware devices for AI inference vary in capabilities and cost, with factors like parallelism, memory size, and power consumption affecting neural network runtime. It’s essential to choose a model architecture aligned with the target hardware’s attributes for optimal performance.

Discover best practices for optimal architecture-hardware alignment: 

  • Benchmark models on your target inference hardware: Understand which model is most suitable for your application. Learn from examples of inconsistent model performance across hardware (e.g., SSD MobileNet and YOLOv5).
  • Optimize your inference hardware performance: Learn how to use tailored configurations and algorithms to maximize the capabilities of your target hardware. Understand the significance of proper hardware settings through detailed case studies, such as the NVIDIA Jetson Xavier NX example, and learn how to ensure optimal inference results every time.

Runtime optimization

While training frameworks like PyTorch and TensorFlow may not be optimized for inference runtime, runtime frameworks like TensorRT, OpenVino, TFLite, TFJS, SNPE, and CoreML are designed to fill this gap. Learn how you can use these frameworks to optimize inference at the runtime level:

  • Convert to Deployment Frameworks: Leverage frameworks like TensorRT, OpenVino, and CoreML to attain hardware-aware optimizations, accelerating your inference time.
  • Employ graph compilation: Use advanced techniques like operator fusion and caching to optimize directed acyclic graphs. Understand your architecture to make your networks run faster. Avoid unnecessary training on non-compilable models.
  • Save on memory and improve latency through quantization: Use Quantization-Aware Training (QAT) to mitigate accuracy degradation by integrating quantization during training, and choose between Standard, Hybrid, or Selective Quantization to balance accuracy, latency, and compatibility.

Model architecture optimization

Algorithmic-level optimization involves refining the structure and design of neural networks to boost their efficiency during inference.

Learn what you can do to optimize inference at the algorithmic level:

  • Optimization Techniques: Techniques like Knowledge Distillation, where a “student” model learns from a “teacher,” and Sparsification and Pruning, which reduce the model size, can enhance inference efficiency.
  • Neural Architecture Search (NAS): While NAS automates the model design process, traditional approaches are resource-intensive.
  • Deci’s AutoNAC Solution: This proprietary technology offers a streamlined, cost-effective approach to NAS, optimizing models for specific applications and hardware without compromising accuracy.

Inference Pipeline Optimization

A model is only as good as its deployment, and real-world deployment involves intricate pipelines. Dive deep into how to streamline this process, from data transformation to the critical server-side decisions that can make or break your application’s performance. Discover how tools such as G-Streamer and NVIDIA DeepStream can help you design complex inference pipelines and face the challenges of scalability and performance.

Pre/post-processing optimization techniques

Get acquainted with effective pre/post-processing optimization techniques: 

  • Using compiled languages: For tasks related to deep learning pre and post-processing, compiled languages like C++ can offer a performance edge over interpreted languages like Python. This is due to the direct compilation into machine code, which often provides faster execution times.
  • Leveraging dedicated accelerators: Hardware accelerators are specifically designed to manage certain tasks with high efficiency. For instance, in image processing, accelerators can handle decoding, resizing, or filtering tasks more efficiently than generic processors.
  • Asynchronous inference:  A technique to maximize throughput, asynchronous inference involves processing the next data batch while the current batch is still being predicted. This not only ensures better GPU utilization but also can lead to enhanced overall performance.

Server-related optimization techniques

Dive into key server-related optimizations that can significantly enhance the efficiency and performance of your deep learning deployments.

  • Client and Server Communication: Discover how to reduce end-to-end latency in your deep learning application by choosing the most effective communication protocol, and understand the trade-offs between options like HTTP and gRPC.
  • Batch Size: Learn the significance of optimizing batch size for efficient inference, and gain insights on how different factors—such as model tasks and memory consumption—can influence throughput across various communication protocols.
  • Serialization: Uncover the importance of efficient serialization in the inference pipeline, and explore best practices to minimize latency using high-performance formats like Protocol Buffers or Apache Arrow.

The Ultimate Guide to Inference Acceleration of Deep Learning-Based Applications

Gain expert insights and actionable tips to optimize deep learning inference for performance and cost.

Deci's Deep Learning Development Platform

Deci's comprehensive inference acceleration capabilities

Deci’s Deep Learning Development Platform, is a comprehensive solution that enables AI professionals to efficiently design, optimize, and deploy top-tier models on any hardware. With tools that streamline every step, from model selection to deployment, Deci ensures a swift transition from data to production-ready models. The platform’s features include: 

  • Hardware benchmarking:​ Benchmark your models’ expected inference performance across multiple hardware types on Deci’s online hardware fleet. Get actionable insights for the ideal hardware and production settings.
  • AutoNAC engine: Get an optimal architecture in days instead of months. Plug in your performance targets, indicate your inference environment and any optimization constraints and let the AutoNAC engine find the optimal neural network for your needs.
  • SuperGradients training library: Train your AutoNAC generated architecture (or any model) with SuperGradients – Deci’s open source PyTorch-based, training library. The library is maintained by Deci’s deep learning experts and includes advanced training techniques that can be easily implemented such as Knowledge Distillation, Exponential Moving Average, Precise Batch Norm, Batch Accumulation, Quantization Aware Training and more.
  • Runtime optimization: Upload any model to the Deci platform and automatically compile and quantize to FP16 or INT8 with a few clicks. Supported frameworks include TensorRT, OpenVino, and CoreML among others.
  • Advanced deployment: Use the Dec’s inference engine to benefit from advanced deployment capabilities such as asynchronous inference pipeline and concurrent inferencing.
  • Unified API: Deploy models with minimal code, easily switch between frameworks, and maintain compatibility with various hardware types.

Gen AI and Computer Vision Case Studies

By using Deci’s suite of inference acceleration solutions, companies with computer vision and NLP applications from industries such as manufacturing, consumer applications, automotive, security, smart city, and energy, among others, have gained unparalleled inference performance. 

Discover their success stories 

Reducing cloud costs and improving UX for text summarization

Learn how, using Deci’s compilation and quantization tools, an AI company improved their text summarization platform’s latency, enhancing user experience and substantially cutting cloud costs.

Scaling up AI-based security camera solution

Using Deci’s AutoNAC engine, a security company enhanced their YOLOX model’s performance, achieving 192 FPS and better accuracy, enabling them to process double the live video streams on their NVIDIA Jetson Xavier NX hardware.

Enabling real-time semantic segmentation for a video conferencing application

Using Deci’s platform, a company tailored a person segmentation model for a Qualcomm® Snapdragon™ 888 board, achieving 3x latency reduction, 4.47x smaller file size, and a 22% reduced memory footprint without compromising accuracy.

Blazing-fast, cost-efficient inference is here

In deep learning inference, many components come into play, including the hardware, software, algorithms, and pipeline, as well as the different techniques that you can implement to optimize each of them. What also affects inference performance are the constraints of real-world production, such as resource availability, application requirements, and operational cost.

To reach the full inference potential of your deep learning applications, it is important to consider all these factors. End-to-end solutions like Deci take a holistic approach to inference acceleration, resulting in the best possible performance that is specific to your needs. Dive deeper into accelerating the inference of your deep learning application and explore Deci’s solution. Download the guide.

The Ultimate Guide to Inference Acceleration of Deep Learning-Based Applications

Gain expert insights and actionable tips to optimize deep learning inference for performance and cost.



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					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")

model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")