Abstract: An immersion cooling server system with AI accelerator apparatuses using in-memory compute chiplet devices. This system includes one or more immersion tanks with heat transfer fluid and configured with at least a condenser device. A plurality of AI accelerator servers is immersed in the heat transfer fluid in a bottom portion of the tanks and is configured to process transformer workloads while cooled by the immersion cooling configuration. Each of the servers includes a plurality of multiprocessors each having at least a first server central processing unit (CPU) and a second server CPU, both of which are coupled to a plurality of switch devices. Each switch device is coupled to a plurality of AI accelerator apparatuses. The apparatus includes one or more chiplets, each of which includes a plurality of digital in-memory compute (DIMC) devices configured to perform high throughput matrix computations for transformer based models.

Abstract: A server system with AI accelerator apparatuses using in-memory compute chiplet devices. The system includes a plurality of multiprocessors each having at least a first server central processing unit (CPU) and a second server CPU, both of which are coupled to a plurality of switch devices. Each switch device is coupled to a plurality of AI accelerator apparatuses. The apparatus includes one or more chiplets, each of which includes a plurality of tiles. Each tile includes a plurality of slices, a CPU, and a hardware dispatch device. Each slice can include a digital in-memory compute (DIMC) device configured to perform high throughput computations. In particular, the DIMC device can be configured to accelerate the computations of attention functions for transformer-based models (a.k.a. transformers) applied to machine learning applications. A single input multiple data (SIMD) device configured to further process the DIMC output and compute softmax functions for the attention functions.

Abstract: Systems and methods for coordinated memory-side cache prefetching and dynamic interleaving configuration modification involve modifying one or both of the prefetch distance or the prefetch degree used by prefetcher modules of one or more memory-side caches by modifying interleaving configuration data following detection of an interleaving reconfiguration trigger condition indicative, for example, of low prefetch accuracy, low prefetch coverage, high prefetch lateness, or a combination of these. In response an interleaving reconfiguration trigger condition, a processor modifies the interleaving configuration data for the processing system based on the prefetch performance characteristics associated with the interleaving reconfiguration trigger condition. In some embodiments, the interleaving configuration data is modified by changing which physical memory address indices are used to determine the bits that define the channel identification number to which that physical memory address is to be mapped.