https://x.com/shao__meng/status/2099281275370754227/photo/1 notes
reinventing ed as next token pred
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