Modern graphics cards pack a lot of functionality. Shading units, Ray tracing, video encoding/deciding. NPU is just the part needed to accelerat Neural nets.
But you can accelerate nural nets better with a GPU, right? They’ve got a lot more parallel matrix multiplication compute than any npu you can slap on a CPU.
It all depends on the GPU. If it’s something integrated in the CPU it will probably not so better, if it’s a 2000$ dedicated GPU with 48GB of VRAM is will be very powerful for Neural Net computing. NPUs are most often implemented as small, low-power, embedded solutions. Their goal is not to compete with data centers or workstations, it’s to enable some basic “AI” features on portable devices. E.g: “smart” camera with object recognition to give you alerts.
Matrix multiplication is also largely what graphics cards do, I wonder how the npus are different.
Modern graphics cards pack a lot of functionality. Shading units, Ray tracing, video encoding/deciding. NPU is just the part needed to accelerat Neural nets.
But you can accelerate nural nets better with a GPU, right? They’ve got a lot more parallel matrix multiplication compute than any npu you can slap on a CPU.
It all depends on the GPU. If it’s something integrated in the CPU it will probably not so better, if it’s a 2000$ dedicated GPU with 48GB of VRAM is will be very powerful for Neural Net computing. NPUs are most often implemented as small, low-power, embedded solutions. Their goal is not to compete with data centers or workstations, it’s to enable some basic “AI” features on portable devices. E.g: “smart” camera with object recognition to give you alerts.