A Matrix Vector Processor is a specialized computing architecture designed to efficiently handle matrix-vector operations, which are fundamental in fields like scientific computing, AI, and signal processing. It is closely related to vector processors, which optimize operations on large arrays of data.
Key Features of Matrix Vector Processors
- Optimized for Linear Algebra: Accelerates matrix-vector multiplications, a common operation in AI and physics simulations.
- Parallel Processing: Uses multiple processing units to handle vectorized computations simultaneously.
- High Throughput: Reduces latency in applications requiring repeated matrix operations.
- Used in AI & Machine Learning: Found in NPUs (Neural Processing Units) and TPUs (Tensor Processing Units) for deep learning workloads.
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