The Problem
Every accelerator speaks a different language. CUDA for NVIDIA GPUs. XLA for Google TPUs. HDL for FPGAs. Proprietary stacks for everything else. Every time you change hardware, or tune the algorithm, your team ends up rewriting the code.
A medical-imaging team rewrites its beamformer in CUDA, then waits days or weeks every time the algorithm changes. An HFT desk maintains FPGA RTL that locks a strategy to one chip. A genomics platform cannot move its alignment pipeline off CPU. An aerospace company wants to use an AI accelerator, but its deterministic signal-processing stack cannot run on the vendor’s AI-only toolchain.
They have solved the speed problem. They have not solved the porting problem. That is what we do.
How It Runs
At VLV Technology, we help teams run non-AI signal-processing algorithms on hardware originally built to accelerate AI.
We do this by mapping deterministic signal-processing operations onto AI-native operators such as convolutions, matrix multiplications, reductions, and pointwise operations. The result is not a trained black box: weights, shapes, and operations are set mathematically, making the implementation transparent, deterministic, and auditable. We first demonstrated this approach through TINA, our award-winning open-source framework for accelerating non-AI algorithms on AI accelerators. We are now building a paid SDK with reusable signal-processing operators and implemented pipelines for domains such as medical imaging, audio processing, DNA analysis, and other deterministic workloads.
Our published results show GPU implementations matching or exceeding hand-written CUDA — while preserving the larger goal of portability across accelerators.
The next step is Rosetta, our planned source-to-source compiler. Rosetta will allow users to write Python-style signal-processing code and automatically convert it into operations from the VLV SDK, enabling deployment on any hardware that accelerates AI, from data-center cards to edge devices.
APPLICATIONS
Medical
Aviation
Medical
Finance
Aviation
Medical