Future of AI Hardware

pandagod-001 — Wed, 27 May 2026 07:38:27 +0000

Over the few days I studied computer architecture and it really changed my view of Artificial Intelligence.

I started with RISC-V International. Learned how processors understand instructions and run programs. What I find interesting about RISC-V is that it is open-source so researchers and startups can make their own hardware for Artificial Intelligence work.
Then I looked at Verilog and digital logic, which showed me how hardware circuits are designed. It was cool to see that Artificial Intelligence accelerators and tensor processors are made from these concepts.

I also learned about simulators like gem5, where researchers test and improve architectures on computers before they even make the hardware. That part really surprised me.
The topic that excited me the most was Artificial Intelligence accelerators like Tensor Processing Units and Neural Processing Units. Modern Artificial Intelligence models need a lot of computing power and normal Central Processing Units are not enough anymore. Special hardware is becoming very important for Artificial Intelligence.
My biggest takeaway is that Artificial Intelligence is not about software anymore. The future is about Artificial Intelligence hardware and software working together as one system. From the compilers to the memory systems, to the accelerators everything is connected now.. That is where the next big innovations will happen.

The Hidden Side of AI Nobody Talks About...

pandagod-001 — Sun, 24 May 2026 14:33:24 +0000

At first I thought Artificial Intelligence was mainly about networks and training models.. After learning about transformers and other things like GPUs and memory systems and compilers and hardware optimization I realized that modern Artificial Intelligence is actually a huge problem that involves a lot of different systems working together.

One of the things I learned is that Artificial Intelligence models mainly do a lot of math problems like matrix multiplications and tensor operations. That is why GPUs are so important. They can do thousands of math problems at the time, which is something that CPUs cannot do.

Having powerful hardware is not enough.
Modern Artificial Intelligence is heavily limited by how we can move data around. GPUs can do math problems quickly but if the data they need to do those math problems does not get to them quickly enough then they just sit there doing nothing. I learned that moving data around can be more expensive than doing the math problems.
I also learned about kernels and compiler optimization and runtimes. There are tools like TVM and MLIR and TensorRT and Triton that help make Artificial Intelligence models work better on types of hardware by reducing how much data needs to be moved around and by making better use of the GPU.
The important thing I learned is this:

Artificial Intelligence is no longer just about building smarter models. It is about using hardware to make intelligence that can be scaled up.
Looking at Artificial Intelligence from a systems perspective made it a lot more interesting, to me than I thought it would be.

Forem: pandagod-001

Future of AI Hardware

The Hidden Side of AI Nobody Talks About...