Diverse Architectures for Unmatched Innovation

• Scalar architecture typically refers to the type of workloads that are optimal on a CPU, where one stream of instructions operates at a given rate typically driven by CPU clock cycles. From system boot and productivity applications to advanced workloads like cryptography and AI, scalar-based CPUs work across a wide range of topographies with consistent, predictable performance.
• Vector architecture is optimal for workloads, which can be decomposed into vectors of instructions or vectors of data elements. GPUs and VPUs deliver vector-based parallel processing to accelerate graphics rendering for gaming, rich media, analytics and deep learning training and inference. By scaling vector architectures from client, data center, and the edge, we can take parallel processing performance from gigaFLOPS to teraFLOPS, petaFLOPS, and exaFLOPS.
• Matrix architecture derives its name from a common operation typically performed for AI workloads (matrix multiplication). While other architectures can execute matrix multiply code, ASICs have traditionally achieved the highest performance implementing the type of operations typically needed for AI inferencing and training, including matrix multiplication.
• Spatial architecture is a special architecture usually associated with an FPGA. Here, the data flows through the chip, and the computing operation performed on the data element is based on the physical location of the data in the device. The specific data transformation algorithm that has been programmed into the FPGA.

Vector Focused: Highly Parallel Processing

Graphics processing units, or GPUs, deliver vector-based parallel processing to accelerate workloads such as real-time graphics rendering for gaming. Because they excel at parallel computing, GPUs are also a good option to accelerate deep learning and other compute intensive workloads.

Spatial Focused: Reprogrammable FPGAs

Field programmable gate arrays, or FPGAs, are integrated circuits that can physically manipulate how their logic gates open and close. The circuitry inside an FPGA chip is not hard etched—it can be reprogrammed as needed.

Intel® FPGAs provide completely customizable hardware acceleration while retaining the flexibility to evolve with rapidly changing computing needs. As blank, modifiable canvases, their purpose and power can be easily adapted again and again.

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