BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
MVP Investment
$9K - $13K
6-10 weeksEngineering
$8,000Cloud Hosting
$240LLM API Credits
$500SaaS Stack
$300Domain & Legal
$1006mo ROI
1-2x
3yr ROI
10-25x
Automation tools have long sales cycles but high retention. Expect $5K MRR by 6mo, accelerating to $500K+ ARR at 3yr as enterprises adopt.
References
References are not available from the internal index yet.
Founder's Pitch
"ArchAgent automates computer architecture discovery with AI-driven cache replacement policy design, outperforming state-of-the-art solutions."
Commercial Viability Breakdown
0-10 scaleHigh Potential
1/4 signals
Quick Build
4/4 signals
Series A Potential
4/4 signals
Sources used for this analysis
arXiv Paper
Full-text PDF analysis of the research paper
GitHub Repository
Code availability, stars, and contributor activity
Citation Network
Semantic Scholar citations and co-citation patterns
Community Predictions
Crowd-sourced unicorn probability assessments
Analysis model: GPT-4o · Last scored: 4/2/2026
🔭 Research Neighborhood
Generating constellation...
~3-8 seconds
Why It Matters
This research addresses the increasing demand for compute by automating and accelerating hardware design processes, which are traditionally human-time-intensive and specialized, while also opening pathways for post-silicon optimization.
Product Angle
Productize ArchAgent as a cloud-based service for semiconductor companies to optimize hardware design, focusing on cache policies and other architecture components.
Disruption
This tool could replace traditional, time-consuming manual design processes in computer architecture by offering a faster, automated alternative, making it a potential disruptor for firms reliant on human-centric design methods.
Product Opportunity
The targeted market includes semiconductor firms and cloud service providers experiencing bottlenecks in compute efficiency. Potential customers include major chip manufacturers and data centers aiming to optimize hardware performance and reduce design cycle costs.
Use Case Idea
Develop a commercial platform that provides automated architecture optimization services to chip manufacturers, enabling faster and more efficient design cycles from pre-silicon to post-silicon phases.
Science
ArchAgent uses agentic AI and evolutionary algorithms to generate and evaluate new computer architecture designs. It creates and improves cache replacement policies by autonomously testing variations and selecting optimal solutions based on performance metrics like IPC speedup.
Method & Eval
ArchAgent was tested by generating cache replacement policies that beat current state-of-the-art solutions on both Google Workload Traces and SPEC 2006 within a notably shorter time frame than manual efforts.
Caveats
The tool relies on accurate simulation and existing benchmarks, which may not cover all potential use-cases or real-world scenarios. Additionally, some generated solutions might not adhere strictly to hardware limitations without careful prompt management.
Author Intelligence
Raghav Gupta
University of California, Berkeley
raghavgupta@berkeley.edu
Akanksha Jain
Google
avjain@google.com
Abraham Gonzalez
Google
abegonzalez@google.com
Alexander Novikov
Google DeepMind
anovikov@google.com
Po-Sen Huang
Google DeepMind
posenhuang@google.com
Matej Balog
Google DeepMind
matejb@google.com
Marvin Eisenberger
Google DeepMind
meisenberger@google.com
Sergey Shirobokov
Google
shirobokov@google.com
Ngân Vũ
Google DeepMind
nganvu@google.com
Martin Dixon
Google
mgdixon@google.com
Borivoje Nikolić
University of California, Berkeley
bora@eecs.berkeley.edu
Parthasarathy Ranganathan
Google
parthas@google.com
Sagar Karandikar
University of California, Berkeley
sagark@eecs.berkeley.edu