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AMD has come up with a modular AI accelerator that can be connected to GPU and cache memory
AMD has registered a patent for a modular Accelerated Processing Device (APD). According to the description, it includes a Machine Learning Accelerator (MLA) chiplet, a GPU block (for example, based on RDNA 3) and a cache memory block (very likely based on AMD Infinity Cache). The only task of APD is to speed up the work of machine learning algorithms, in particular, in tasks related to matrix multiplication.
As indicated by the resource TechPowerUp, a modular unit for accelerating machine learning algorithms will be able to perform functions similar to tensor cores used in NVIDIA GPUs. The latter, for example, are responsible for the tasks of working with functions such as DLSS.
The feature of the modular APD block will allow AMD to avoid some of the problems associated with its use directly as part of the GPU itself. One of them, for example, is the inevitable increase in the area of the crystal and, as a consequence, the cost of its production.
In addition, modularity will allow APD to be used as part of other products, and not only as part of graphics platforms. For example, the patent indicates the possibility of its use in central processors. For this, it is proposed to use APD in the form of an additional chiplet based on the 12 nm process technology. In addition, APD can perform tasks to speed up the processing of cache requests between the GPU and the cache chiplet, and can also be used as a cache memory or directly addressable memory.