Created first draft of outline for efficient AI

efficient_ai.qmd

@@ -1,2 +1,113 @@
 # Efficient AI
 
+## Introduction
+
+Explanation: The introduction sets the stage for the entire chapter, offering readers an insight into the critical role efficiency plays in the sphere of AI. It outlines the core objectives of the chapter, providing context and framing the ensuing discussion.
+
+- Background and Importance of Efficiency in AI
+- Discuss how Cloud, Edge and TinyML differ (again)
+
+## The Need for Efficient AI
+
+Explanation: This section articulates the pressing necessity for efficiency in AI systems, particularly in resource-constrained environments. Discussing these aspects will underline the crucial role of efficient AI in modern technology deployments, facilitating a smooth transition to discussing potential approaches in the next section.
+
+- Resource Constraints in Embedded Systems
+- Energy Efficiency
+- Computational Efficiency
+- Latency Reduction
+- Real-time Processing Requirements
+
+## Approaches to Efficient AI
+
+Explanation: After establishing the necessity for efficient AI, this section delves into various strategies and methodologies to achieve it. It explores the technical avenues available to optimize AI models and algorithms, thus serving as a bridge between the identified needs and the practical solutions presented in the following sections on specific efficient AI models.
+
+- Algorithm Optimization
+- Model Compression
+- Hardware-Aware Neural Architecture Search (NAS)
+- Compiler Optimizations for AI
+- ML for ML Systems
+
+## Efficient AI Models
+
+Explanation: This section offers an in-depth exploration of different AI models that are designed to be efficient in terms of computational resources and energy. It not only discusses the models but also offers insights into how they are optimized, thus preparing the ground for the benchmarking and evaluation section where these models are assessed and compared.
+
+- Model compression techniques
+  - Pruning
+  - Quantization
+  - Knowledge distillation
+- Efficient model architectures
+  - MobileNet
+  - SqueezeNet
+  - ResNet variants
+
+## Efficient Inference
+
+- Optimized inference engines
+  - TPUs
+  - Edge TPU
+  - NN accelerators
+- Model optimizations
+  - Quantization
+  - Pruning
+  - Neural architecture search 
+- Framework optimizations
+  - TensorFlow Lite
+  - PyTorch Mobile
+
+## Efficient Training
+
+- Techniques
+  - Pruning
+  - Quantization-aware training
+  - Knowledge distillation
+- Low precision training
+  - FP16
+  - INT8
+  - Lower bit widths
+
+## Benchmarking and Evaluation of AI Models
+
+Explanation: This part of the chapter emphasizes the importance of evaluating the efficiency of AI models using appropriate metrics and benchmarks. This process is vital to ensuring the effectiveness of the approaches discussed earlier and seamlessly connects with case studies where these benchmarks can be seen in a real-world context.
+
+- Metrics for Efficiency
+  - FLOPs (Floating Point Operations)
+  - Memory Usage
+  - Power Consumption
+  - Inference Time
+- Benchmark Datasets and Tools
+- Comparative Analysis of AI Models
+- EEMBC, MLPerf Tiny, Edge
+
+## Caveat on Efficiency Metrics
+
+Explanation: This section emphasizes the diverse aspects that constitute "efficiency" in machine learning systems. It aims to guide readers in identifying the crucial metrics that matter, depending on the specific use case, and underscores the importance of considering these metrics early in the ML workflow.
+
+- Multi-faceted nature of efficiency in ML systems
+  - Beyond accuracy: various critical metrics
+  - Latency as a pivotal component 
+- Importance of low latency in real-time applications
+  - Specific application dictates acceptable latency
+- Power efficiency in embedded systems
+  - Strategies for extending battery life
+  - Role of specialized hardware 
+- Considerations for cost-efficient deployments
+  - Hardware costs vs. model accuracy 
+  - Balancing accuracy, latency, and costs 
+- Tailoring efficiency to the product
+  - Comparison: automotive, mobile, smart home applications
+  - Distinct constraints necessitate diverse efficiency approaches
+- Early integration of efficiency metrics in ML workflow
+  - Influence on architecture, hardware, and algorithm selection
+  - Proactive consideration of efficiency metrics
+  
+## Emerging Directions
+
+- Automated model search
+- Multi-task learning  
+- Meta learning
+- Lottery ticket hypothesis
+- Hardware-algorithm co-design
+
+## Conclusion
+
+Explanation: This section synthesizes the information presented throughout the chapter, offering a coherent summary and emphasizing the critical takeaways. It helps in consolidating the knowledge acquired, setting the stage for the subsequent chapters on optimization and deployment.