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on
1
Introduction
2
How we build a microprocessor
3
Microprocessor complexity
4
Micro architecture
5
Optimization
6
Complex design
7
Managing multiple versions
8
Design Data
9
Data Model Integration
10
Python Environment
11
Engineering View
12
Engineering View Example
13
Custom Analysis Example
14
Load Multiple Data Files
15
Developers Experience
16
Debugging
17
Checking for changes
18
Vectorized approach
19
democratized analysis
20
references
21
extensions
22
conclusion
Description:
Explore the challenges and solutions in analyzing large-scale data for high-performance microprocessor design in this PyCon US talk. Dive into the Design Data (DD) data model, a technological breakthrough addressing key challenges in integrated circuit design, analysis, and debugging. Learn how this custom domain-specific read-only binary data model, implemented using C++, CPython, and Python method bindings, efficiently manages design components, reporting, data linking across time, and provides a reliable, scalable platform. Discover how the C++ implementation enables efficient graph traversal, custom interactive analysis, and design graph visualization. Understand the benefits of compressible binary format for version comparison in multi-year projects. Gain insights into integrating modern Free Open-Source Software technologies into complex Electronic Design Automation (EDA) ecosystems. Get inspired to experiment with C/C++ and CPython bindings in your application workflow and explore innovative ways to integrate Data Science methods into your domain. Read more

Leveraging a Custom CPython Data Model for High Performance

PyCon US
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