Neuromorphic Memory Layer for Low-Latency Die-to-Host Transfer
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Solution Overview
Problem
Current DRAM memory devices face limitations in capacity, speed, and cost, necessitating improvements in main memory technologies to meet increasing demand for faster and more efficient data processing.
Innovation Solution
The integration of a neuromorphic layer between memory dies and a host interface, facilitated by a buffer that manages data speeds and reallocates connections to accommodate both high-speed and wide-data interactions, enabling local processing closer to memory storage and reducing latency and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is transferred from memory dies to host interface through traditional architecture, then data transfer occurs, but transfer distance is long and latency is high
Solution Approach 1:
A neuromorphic layer is introduced as an intermediary component between the memory dies and the host interface. This neuromorphic layer performs local data processing and transformation, reducing the need for long-distance data transfers to the host interface and thereby reducing latency and improving transfer speed.
Solution Approach 2:
The patent introduces a new architectural dimension by adding the neuromorphic layer that operates in parallel with the traditional memory hierarchy. This creates an additional processing dimension that handles data locally before host interface communication, effectively reducing the critical path length for data access.
2Productivity
If buffer reallocates connections for high-speed and wide-data interactions, then data processing capability increases, but buffer complexity increases
Solution Approach 1:
The buffer is segmented into multiple specialized sub-buffers or connection groups, each optimized for specific data transfer patterns (high-speed vs. wide-data interactions). This segmentation allows independent optimization of each segment while managing overall complexity through modular organization.
Solution Approach 2:
The buffer employs dynamic connection allocation where connection paths are reconfigured based on real-time data transfer requirements. This dynamic behavior allows the buffer to adapt to different operational modes (high-speed vs. wide-data) without requiring separate static infrastructure for each mode.
3Loss of time
If neuromorphic layer is integrated between memory dies and host interface, then local processing is enabled and latency is reduced, but device complexity increases
Solution Approach 1:
The neuromorphic layer is merged with the existing memory die structure, sharing physical infrastructure and control logic where possible. This integration approach enables local processing functionality while reusing existing interconnect resources and reducing the overhead of completely separate processing units.
Solution Approach 2:
The neuromorphic layer is designed to perform multiple functions including local data processing, data transformation, and interface protocol conversion. This multi-functionality reduces the need for separate specialized components, thereby managing device complexity while providing comprehensive local processing capabilities.
Data Source
AI summary
Apparatus and methods are disclosed, including memory devices and systems. Example memory devices, systems and methods include a stack of memory dies, a controller die, and a buffer. Example memory devices, systems and methods include one or more neuromorphic layers logically coupled between one or more dies in the stack of memory dies and a host interface of the controller die.