Vector Processing Unit Layout for High-Bandwidth Local Memory
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Solution Overview
Problem
Conventional vector processing units (VPUs) face limitations in flexibility, memory bandwidth, and computational density, which hinder efficient performance in computations associated with neural networks and other vectorized operations.
Innovation Solution
A vector processing unit (VPU) architecture that includes tightly coupled processor units and memory banks, enabling high-bandwidth data processing and arithmetic operations through localized computational resources, with distinct configurations for SIMD, MXU, and XU units to optimize data throughput and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional vector processing units are used, then basic vector computations can be performed, but flexibility, memory bandwidth, and computational density are limited
Solution Approach 1:
The VPU is segmented into multiple processor units (PUs), each capable of independent vectorized computations. Each PU contains arithmetic logic units, register files, and instruction decoders that can operate autonomously on different data segments, enabling flexible parallel processing while maintaining high computational density through modular architecture
Solution Approach 2:
The patent introduces a three-dimensional data organization approach with vector lanes, element positions, and time cycles. Multiple processor units operate simultaneously on different lanes, with each lane processing multiple elements through time-multiplexed arithmetic logic units, effectively adding temporal and spatial dimensions to the computational architecture
2Productivity
If memory bandwidth is increased for faster data access, then data throughput improves, but device complexity and area increase
Solution Approach 1:
Multiple memory banks are merged into a unified vector memory structure that serves all processor units. The memory system uses shared control logic and combined data paths to provide high-bandwidth access to multiple PUs simultaneously, achieving aggregate throughput proportional to the number of PUs without proportionally increasing total memory area
Solution Approach 2:
The arithmetic logic units are designed as universal resources that can perform multiple operations (addition, subtraction, multiplication, division) on different data types (integers, floating-point). Time-multiplexing allows the same ALU to serve multiple vector lanes sequentially, reducing the total number of ALUs needed while maintaining high computational throughput
3Speed
If computational density is increased for faster processing, then processing speed improves, but flexibility and adaptability decrease
Solution Approach 1:
The VPU employs dynamic instruction decoding and operation selection where each processor unit can adapt its arithmetic logic unit operations based on the current instruction type. The control logic dynamically configures data flow paths and ALU operations to match the specific computational requirements, enabling high-speed processing of diverse vector operations without sacrificing flexibility
Solution Approach 2:
Register files serve as intermediary storage between memory and arithmetic logic units, buffering data to decouple memory access timing from computation timing. This allows ALUs to operate at maximum speed using data from registers while memory accesses occur independently, maintaining both high processing speed and flexibility for different memory access patterns
4Loss of time
If processor units are tightly coupled with memory for high bandwidth, then data exchange speed improves, but device complexity increases
Solution Approach 1:
Each processor unit is locally coupled to the shared vector memory with dedicated data paths and control signals. This local coupling minimizes access latency for each PU while the shared memory structure prevents proportional increases in overall system complexity through resource sharing and unified control logic
Data Source
AI summary
A vector processing unit is described, and includes processor units that each include multiple processing resources. The processor units are each configured to perform arithmetic operations associated with vectorized computations. The vector processing unit includes a vector memory in data communication with each of the processor units and their respective processing resources. The vector memory includes memory banks configured to store data used by each of the processor units to perform the arithmetic operations. The processor units and the vector memory are tightly coupled within an area of the vector processing unit such that data communications are exchanged at a high bandwidth based on the placement of respective processor units relative to one another, and based on the placement of the vector memory relative to each processor unit.