Machine Learning Multimedia Accelerator for Low-Latency Preprocessing
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Solution Overview
Problem
Existing machine learning processes face inefficiencies in terms of area, power usage, and latency due to the need for extensive read and write operations to storage devices, which can be exacerbated by the use of general-purpose processors like CPUs and GPUs.
Innovation Solution
Implementing a multimedia hardware accelerator within a compute cluster of a system on a chip, which includes a multimedia hardware accelerator and matrix processors like TPUs, to offload pre- and post-processing tasks, thereby reducing the burden on general-purpose processors and optimizing operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If general-purpose processors (CPU, GPU) are used for machine learning operations, then processing flexibility is maintained, but latency and power consumption increase
Solution Approach 1:
The system segments processing tasks by creating separate compute clusters dedicated to specific functions: one cluster handles multimedia pre-processing operations while another cluster handles matrix processing for machine learning. This segmentation allows each cluster to be optimized for its specific task, reducing overall latency while maintaining system versatility.
Solution Approach 2:
A buffer is introduced as an intermediary component between storage devices and compute clusters. The buffer stores multimedia data and provides it to compute clusters, reducing the need for frequent read operations from storage devices. This intermediary mechanism decreases latency by eliminating repeated storage access while maintaining data availability for flexible processing.
2Adaptability or versatility
If general-purpose processors (CPU, GPU) are used for machine learning operations, then processing versatility is maintained, but power consumption increases
Solution Approach 1:
The system divides processing workloads into separate specialized clusters: one for multimedia pre-processing and another for matrix operations. Each cluster can be powered independently and optimized for its specific function, reducing overall power consumption compared to using high-power general-purpose processors for all tasks.
Solution Approach 2:
Pre-processing tasks are extracted from the main machine learning processing path and handled by a dedicated multimedia pre-processing compute cluster. This extraction allows the main matrix processing cluster to focus only on computationally intensive ML operations, reducing total power consumption while maintaining processing versatility.
3Ease of operation
If frequent read and write operations to storage devices are performed, then data access is enabled, but latency and power usage increase
Solution Approach 1:
A buffer is introduced as an intermediary between storage devices and compute clusters. The buffer pre-loads and caches multimedia data, allowing compute clusters to access data without frequent storage device accesses. This maintains ease of data access while dramatically reducing latency associated with repeated storage operations.
Solution Approach 2:
The buffer performs preliminary data loading and caching operations before the compute clusters need the data. By pre-positioning data in the buffer, the system eliminates the need for frequent read operations during processing, reducing latency while maintaining continuous data availability.
4Ease of operation
If frequent read and write operations to storage devices are performed, then data access is enabled, but power consumption increases
Solution Approach 1:
The buffer acts as an intermediary data repository between storage devices and compute clusters. It maintains data in an easily accessible state without requiring continuous high-power storage device access, thus reducing power consumption while maintaining data access capability.
Solution Approach 2:
Data is preliminarily loaded into the buffer before processing begins. This preliminary action eliminates the need for continuous high-power storage device access during processing, reducing overall power consumption while maintaining data accessibility for the compute clusters.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for machine learning multimedia hardware acceleration. One of the methods includes retrieving multimedia data using a multimedia hardware accelerator of a first compute cluster of a system on a chip, wherein the first compute cluster includes (i) the multimedia hardware accelerator and (ii) one or more matrix processors that perform machine learning operations; performing a first set of operations using the multimedia hardware accelerator of the first compute cluster and the retrieved multimedia data, wherein the first set of operations include pre-processing the retrieved multimedia data, wherein the multimedia hardware accelerator includes circuit elements that are each configured to perform a respective operation in the first set of operations; processing, using the one or more matrix processors of the first compute cluster, the pre-processed multimedia data; and generating, by processing the pre-processed multimedia data, a model trained for predicting features of subsequent multimedia data.