Strategic Objectives
• Master the mathematical transformation of weights from FP32 to INT8 and FP4.
• Minimize accuracy loss while maximizing throughput on specialized hardware.
• Understand the thermal and power dynamics of quantized inference.
• Bridge the gap between high-level algorithms and low-level circuit efficiency.
The Core Challenge
General neural networks are too massive and power-hungry for edge devices, creating a bottleneck between theory and physical reality.
01
The Foundations of Precision
02
The Arithmetic of AI
03
The Silicon Bottleneck
04
Uniform Quantization Schemes
05
Non-Uniform and Logarithmic Scaling
06
Post-Training Quantization (PTQ)
07
Quantization-Aware Training (QAT)
08
The 8-Bit Standard (INT8)
09
Pushing Boundaries with FP4
10
Stochastic Rounding Techniques
11
Dynamic Range and Scaling Factors
12
Hardware Accelerators for Quantization
13
Vectorization and Parallelism
14
The Impact of Sparsity
15
Entropy and Information Loss
16
The Power-Precision Trade-off
17
Mixed-Precision Architectures
18
Error Propagation in Deep Nets
19
On-Device Inference Engines
20
Verification and Benchmarking
21