Arm Yourself: Heterogeneous Compute Ushers in 150x Higher Performance

The premise of this research is that Heterogeneous Compute (HC) is deployed today in consumer mobile devices and can increase the performance of Matrix Workloads by a factor of 50 compared with traditional architectures. Also, this improves price-performance and power consumption by a factor of over 50. Enterprise HC is likely to use the same technologies as consumer HC. As a result, the Heterogeneous Compute improvements will dramatically increase the value of real-time Matrix Workloads, and especially the subset of AI inference workloads.

The more strategic premise is that while Matrix Compute can reduce the cost of processing the data by a factor of fifty (50), the cost of storing and moving that data will be 50 times higher. If Matrix Workloads use traditional datacenter processes, the cost of the non-processor components will dominate. However, by using a Data-led Operational Architecture (DLOA), the storage and networking costs can be brought in line with the compute costs.

The result of these two fundamental architectural changes is to allow real-time Matrix Workloads to process two orders of magnitude more data with the same cost envelope as traditional enterprise computing. Heterogeneous Compute running Matrix Workloads are vital technologies for data-led enterprises.