Tensor Processing Unit Parallel Execution Architecture
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Solution Overview
Problem
Machine learning algorithms require a large quantity of training data and extensive processing time to achieve satisfactory results, necessitating a solution to accelerate the learning process.
Innovation Solution
The development of a Hardware Accelerated Machine Learning architecture, specifically a Tensor Processing Unit (TPU), which includes a semiconductor device designed to efficiently execute tensor contraction operations and other machine learning-specific operations, utilizing pipelining and specialized hardware to reduce clock cycles and enhance performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning algorithms process large quantities of training data with sufficient repetitions, then learning accuracy improves, but processing time increases significantly
Solution Approach 1:
The patent segments the machine learning processing workload across multiple processing units organized in arrays. Each processing unit handles specific tensor operations independently, allowing parallel processing of training data batches. This segmentation enables simultaneous processing of multiple data samples and features, dramatically reducing the time required to achieve sufficient learning repetitions while maintaining accuracy.
Solution Approach 2:
The patent introduces a hardware dimension to accelerate machine learning by implementing dedicated tensor processing units and specialized memory architectures. The system adds temporal parallelism through pipelined processing stages and spatial parallelism through multiple processing elements, transforming the single-threaded sequential processing into multi-dimensional concurrent operations that reduce overall processing time.
2Adaptability or versatility
If general-purpose processors are used for machine learning operations, then system versatility is maintained, but processing speed and efficiency deteriorate
Solution Approach 1:
The patent implements processing units that can perform multiple tensor operations including matrix multiplication, convolution, and element-wise operations. The same hardware array can be reconfigured through different activation patterns and data layouts to handle various machine learning workloads, maintaining versatility while achieving high-speed specialized processing for each operation type.
Solution Approach 2:
The patent replaces general-purpose software-based processing with dedicated hardware tensor processing units. This substitution transforms software interpretation overhead into hardware-level parallel computation, achieving speedups of hundreds of times for tensor operations while maintaining the ability to execute different algorithms through software control of the hardware arrays.
3Productivity
If more processing power is allocated to machine learning operations, then learning speed improves, but hardware complexity and resource requirements increase
Solution Approach 1:
The patent employs homogeneous arrays of identical processing units that perform the same basic tensor operations. This homogeneity simplifies the control logic and memory interface design compared to heterogeneous systems, as all units can be managed through uniform addressing and control signals. The regular structure enables efficient resource utilization and reduces the complexity of interconnect and synchronization mechanisms.
Solution Approach 2:
The patent combines multiple processing functions into unified tensor processing units that can perform multiplication, accumulation, and activation operations within the same hardware block. By merging these functions and using shared memory resources and control logic, the system achieves high processing power without proportionally increasing overall hardware complexity.
Data Source
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AI summary
An architecture and associated techniques of an apparatus for hardware accelerated machine learning are disclosed. The architecture features multiple memory banks storing tensor data. The tensor data may be concurrently fetched by a number of execution units working in parallel. Each operational unit supports an instruction set specific to certain primitive operations for machine learning. An instruction decoder is employed to decode a machine learning instruction and reveal one or more of the primitive operations to be performed by the execution units, as well as the memory addresses of the operands of the primitive operations as stored in the memory banks. The primitive operations, upon performed or executed by the execution units, may generate some output that can be saved into the memory banks. The fetching of the operands and the saving of the output may involve permutation and duplication of the data elements involved.