The Role of Machine Learning in Electronic Design Automation
OpeNPDN: A Neural-network-based Framework for Power Delivery Network Synthesis
Thermal and IR Drop Analysis Using Convolutional Encoder-Decoder Networks
A Survey of Circuit Foundation Model: Foundation AI Models for VLSI Circuit Design and EDA
https://kjfeng.me/blog/industry-job-search.html
This sequence illustrates the standard progression pathway for early-career researchers looking to break into the AI Safety ecosystem. It charts a trajectory from part-time remote research to prestigious, full-time independent fellowships, and finally into direct industry or lab-based roles. [1]
When the meetings are recorded, can it be in a database so that is queryable or comes up with solutions to prior problems or new areas of exploration?
Can Handoff heavy be managed well if about 300 claude agents manage the design?
Knowing exactly what to remember and what to forget and when to be "Fifty first dates"
https://readable-intelligence.com/
https://felixrieseberg.com/how-to-be-more-ambitious-a-tutorial-for-humans-with-models/
https://www.chiplog.io/p/why-stacked-3d-dram-is-the-right
https://www.thonking.ai/p/what-shapes-do-matrix-multiplications
https://www.thonking.ai/p/strangely-matrix-multiplications - flops/watt
https://siboehm.com/articles/22/CUDA-MMM
https://x.com/bradenjhancock/status/2104209695200485432 - shape of language model
Compiler and Hardware Co-Design for Accelerator Architectures
https://udlbook.github.io/udlbook/ - understanding deep learning
compilers for machine learning
https://nanjiang.cs.illinois.edu/2026/09/30/proof.html - Breaking down proofs
https://x.com/shao__meng/status/2099281275370754227/photo/1 notes
reinventing ed as next token pred
https://www.interconnects.ai/p/open-source-ai-reading-list
https://deep-learning-alchemy.github.io/ from 9/28
https://x.com/gordic_aleksa/status/2099884333305860516 - "double descent can be understood as a phase transition between memorizing individual examples and learning reusable structure from the data"
https://web.stanford.edu/class/msande319/ -
This is an unusually good foundation for this job. It covers machine code, performance evaluation/optimization, computer arithmetic, memory organization, networking and concurrency.
If you don't already have a strong systems background, I would do this before diving deeply into SoCs.
Then:
This is much closer to the actual Meta position. CMU describes it as a deep treatment of modern computer-system design, with particular emphasis on parallel systems and hardware/software interaction.
CMU 15-740 Computer Architecture
And if you become really serious:
This is the research-level version: state-of-the-art architecture, architecture research, and a semester research project.
You do not need the PhD course to prepare for the job, though. 15-740 is the sweet spot.
This is probably the one book I'd buy/read for this job.
The important word is quantitative.
You're not merely learning:
"A cache is faster than DRAM."
You're learning to ask:
"How much faster?"
"Under what workload?"
"What happens when 8 agents contend for the memory system?"
"How much bandwidth do we actually need?"
"Is the bottleneck computation, latency, bandwidth, or interconnect?"
That's exactly the mindset of the job.
This job specifically says:
SoC architecture, NoCs, memory subsystem, QoS
I'd spend an enormous amount of time here.
You want to be comfortable with:
CPU/accelerator → cache → LLC → memory controller → DRAM
and understand:
MIT's computer architecture material is a good accessible starting point; its memory-hierarchy material explicitly frames memory as a pipeline bottleneck and explains the hierarchy used to alleviate it.
This is one of the first performance-analysis concepts I'd make second nature.
The fundamental question is:
Is my workload compute-bound or memory-bound?
The Roofline model expresses this through arithmetic intensity:
operations / bytes moved
Low arithmetic intensity → memory bandwidth becomes the ceiling.
High arithmetic intensity → compute throughput becomes the ceiling.
NVIDIA has a very good practical explanation, including hierarchical Roofline analysis that lets you reason about different levels of the memory hierarchy.
NVIDIA Roofline performance analysis
This is worth learning by actually measuring programs, not just reading about it.
https://www.siliconcodesign.com/p/an-advanced-system-architecture-breakdown
https://x.com/jasonschips/status/2095084434185924625 - Hot chips 2026
https://x.com/QQ_Timmy/status/2095128760542790121 - Google training and inference
Stay tuned for English version book --- https://x.com/leafmeta/status/2083768641145938336
A graph contains not only what to do but also what is known. Furthermore, the latter is a much older concept—the lineage leading from knowledge graphs to ontology and the semantic web. Therefore, this book divides graph engineering into two tracks.
Knowledge Graph Engineering Track 1: What the Model Knows; Ontology, Schema, Source; Time, Quality, Verification.
Agent Graph Engineering Track 2: What the Model Does; State, Reducer, Superstep; Tools, Authority, Termination Conditions. Both are built with nodes and edges.
Here is how on-chip controllers monitor and react to degrading timing slack in real time.
After a red eye flight, our five year old was tired. We were meeting friends in the new york city during the layover of international travel.
When the city was just waking up on a holiday, our friend bought us breakfast from a food stand.As we were leaving with our food, the stand owner gave our toddler a donut. He refused money for it. Intended to make the kid happy but it made a strong impression on us. A welcome to the city!!
I have been to the city many times hauling back a suticase full of second hand books from The Strand Store, Times square visit that I rendered in a poem, but that donut!!
10 years later
Any parent with a kid needs to know, where the restrooms are!! Our fiend dropped us at an entrance. There were few levels in the building. The receptionist said that the closest restroom was at a place called Occ.. something.
We started running, from one way pointer to another. It was a miracle that so much running could be done with a full bladder. Finally, the apple store pointer helped. Later in the evening, we were being shown around the Occulus, which we had seen 10 years ago, and as we were walking through it, our eyes fell on the Apple store sign and my younger one and I gave out a big laugh of the place etched in our minds, coming full circle in many ways.
https://x.com/MillieMarconnni/status/2071529847189582018
https://x.com/Jolyne_AI/status/2071398251480359020 - Mathematical Foundations of Reinforcement Learning
https://x.com/YiMaTweets/status/2063078487494398037
https://ma-lab-berkeley.github.io/deep-representation-learning-book/
https://blog.eleuther.ai/transformer-math/ - factor of 6
https://github.com/stas00/ml-engineering/blob/master/training/performance/README.md
https://lmms-engine.readthedocs.io/en/latest/reference/mfu.html
Due to the increased wealth produced by the country, the need for currency also grew. from a translated substack.
Someone once interpreted what I was saying as "thought is money."
What I was trying to describe was that human civilization evolves through increasingly powerful ways of organizing thought.
The human brain is remarkable because it can create models of reality. We think, imagine, plan, and solve problems. In many ways, artificial intelligence is humanity's attempt to replicate or extend that capability. AI is essentially an effort to scale cognition itself.
But once you have intelligence, another challenge immediately appears: how do you organize it?
A single mind can accomplish only so much. Civilization emerged when humans learned to coordinate many minds together. The next great step after thought was organization.
Throughout history, we have invented different systems to organize human activity.
Language allowed people to share ideas.
Writing allowed knowledge to survive across generations.
Politics emerged as a way to organize large groups of people whose interests were not always aligned.
As societies became largeranother invention transformed coordination: currency.
Before money, exchange was limited and inefficient. Currency created a common language of value. It enabled strangers to cooperate without needing personal relationships or direct barter. Wealth accumulation became possible on a scale never seen before.
Over time, the pursuit of wealth became a central organizing force in many societies. Entire economies, institutions, and governments began orienting themselves around growth, production, and capital.
Yet money itself was never the final goal. It was simply another coordination technology.
Seen through this lens, history can be viewed as a sequence of increasingly sophisticated coordination systems:
Each step expands humanity's ability to act collectively.
Today, we may be entering another transition.
For centuries, intelligence was scarce. Human expertise was limited by the number of trained people available. AI changes that equation. If cognitive capability becomes abundant, then intelligence itself may no longer be the primary constraint.
The bottlenecks shift elsewhere.
Now we hear discussions about data centers, energy production, semiconductor manufacturing, electrical grids, and geopolitical influence. These are not separate from the AI story; they are the next layer of it.
When cognition becomes easier to produce, the scarce resources become the infrastructure required to support it.
In previous eras, land was power.
Then labor was power.
Then capital was power.
Today, computation, energy, and the ability to govern complex systems may become the defining sources of power.
This is why AI discussions quickly become discussions about politics, economics, energy, and global strategy. Once intelligence can be scaled, organization becomes the central challenge again.
Perhaps the history of civilization is not primarily a story of technology, money, or governments. Perhaps it is a story of coordination.
Human thought created systems of organization. Politics organized people. Currency organized incentives. Computers organized information. AI organizes cognition.
And as cognition becomes increasingly abundant, power, energy, infrastructure, and governance become the next frontiers of human coordination.
Your Decision Making Is All Wrong
what is the % of optimizers vs sastisficers
do we have consistent style or are we one in some areas of life and another in the rest?
There is a quick feedback loop when it comes to selecting movies to watch.
In medical, legal and other such fields, there is a need for second opinion. But some constraints make it easier. 3 Medical Routines That Older People May Not Need
Matteo Ricci
inscriptions on the walls
Its hard to improve in a foreign language if you can't read it easily
universal contact language - culturally neutral - math?
https://x.com/Lawfull_neutral/status/2054123260200337829 - Need those dead languages in medicine and law.
