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Dijo Torres’s October 5, 2026 post is a personal kickoff, not an embedded-systems tutorial: after more than a year away from C and C++, he is returning with small math problems and plans to document what he learns in public. The spark is a practical question about local AI inference—what work a PC’s GPU does, and how that connects to the math and systems behind machine-learning accelerators.
Why Torres is returning to C++
Torres says it had been more than a year since he last coded in C or C++. Rather than begin with a large project, he chose small mathematical problems as a way back into C++ practice. That modest starting point gives the post its shape: it is about rebuilding momentum and following curiosity, not presenting a finished curriculum.
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How local inference led to accelerator questions
Curiosity about how long his PC’s GPU would take to process a local request prompted Torres to look into GPUs, TPUs, and NPUs. That hardware question led to a deeper one: what computations are these accelerators built to perform? His path points toward matrix multiplication, a central workload in machine learning, and then toward methods and systems used to carry it out efficiently.
From matrix multiplication to the surrounding systems
The post sketches a chain of topics rather than explaining them as a technical reference. Torres mentions GEMM (general matrix multiplication), BLAS (a family of basic linear algebra routines), systolic arrays, XLA, MXUs, neuromorphic chips, cuDNN, and CUTLASS. Taken together, these names suggest the range of questions he hopes to explore: algorithms, accelerator architecture, compilers, and software libraries. The post does not compare these tools or establish which is best for a particular device or task.
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The AlphaTensor example that caught his attention
To illustrate the possibility of improving matrix multiplication, Torres points to AlphaTensor, a Google DeepMind result he describes as using reinforcement learning to find algorithms that need fewer multiplications than previously known methods. His post reports a 4×4 modular-arithmetic example using 47 multiplications, compared with 49 for Strassen’s algorithm. Those figures are the example as reported in Torres’s post; they should not be read as a general performance comparison or independently verified result here.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A public learning log, not a finished guide
Torres’s stated next step is to share the process as it unfolds: “Starting today, I’m going to document everything I learn here, in public.” That makes the post an invitation to follow an early learning journey. It does not yet offer board recommendations, performance tests, or step-by-step embedded development instructions.
Read the original post by Dijo Torres on DEV Community.
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