AI Accelerator Memory Data Transfer Latency
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Solution Overview
Problem
Existing memory systems face challenges in efficiently transferring data during artificial intelligence (AI) operations, leading to increased latency and power consumption.
Innovation Solution
Incorporating an AI accelerator within memory devices that includes hardware, software, and firmware to perform AI operations, with the ability to transfer data between memory devices performing AI operations, thereby reducing the need for external processing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is transferred between memory devices during AI operations, then AI operations can be distributed across multiple devices, but latency increases due to the data transfer time
Solution Approach 1:
The patent divides the AI accelerator functionality into distributed segments across multiple memory devices. Each memory device contains its own AI accelerator that can independently perform AI operations on local data, while only transferring essential intermediate results rather than entire datasets. This segmentation reduces transfer volume and latency while maintaining distributed processing capability.
2Productivity
If data is transferred between memory devices during AI operations, then processing capacity can be scaled, but power consumption increases due to data transfer operations
Solution Approach 1:
By segmenting the AI processing workload across multiple memory devices with integrated AI accelerators, the system performs computations locally rather than centralizing data transfers. Each device processes its assigned data segment independently, minimizing inter-device transfer requirements and reducing overall power consumption while maintaining scalable processing capacity.
Solution Approach 2:
Each memory device with an AI accelerator serves itself by performing AI operations locally on stored data without requiring constant data retrieval from external sources. The AI accelerator within each device utilizes the device's own memory resources and processing capabilities, reducing dependency on inter-device data transfers and lowering system-wide power consumption.
3Productivity
If an AI accelerator is integrated within memory devices, then data transfer with host is minimized, but device complexity increases
Solution Approach 1:
The patent merges the AI accelerator functionality directly into the memory device structure, combining storage and processing capabilities in a single integrated unit. This consolidation eliminates the need for separate AI processing hardware and reduces data transfer between distinct components, improving efficiency while managing complexity through unified device architecture.
Solution Approach 2:
The memory device is designed with multi-functionality, serving both as a storage device and an AI processing unit. The integrated AI accelerator enables the memory device to perform traditional memory functions alongside AI computations, reducing the need for specialized dedicated AI hardware and simplifying the overall system architecture despite the added processing capability.
Data Source
AI summary
The present disclosure includes apparatuses and methods related to transferring data in a memory system with an artificial intelligence (AI) mode. An apparatus can receive a command indicating that the apparatus operate in an artificial intelligence (AI) mode, a command to perform AI operations using an AI accelerator based on a status of a number of registers, and a command to transfer data between memory devices that are performing an AI operation. The memory system can transfer output data of a layer and/or neuron of an AI operation from a first memory device to a second memory device; and the second memory device can use the output data transferred to the second memory device as input data for a subsequent layer and/or neuron of the AI operation.