Multi-Die AI Chip Package for Neural Network Layer Processing
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Solution Overview
Problem
The design and production of custom application-specific integrated circuits (ASICs) for neural networks face challenges such as long design times and significant non-recurring engineering costs, which are exacerbated by the need for small volume production.
Innovation Solution
A standard die configured for processing neural network tasks is used, with multiple identical dies interconnected in a single ASIC chip package, each associated with different layers of the neural network, allowing for efficient processing and reducing design time and engineering costs by enabling the same die to be used across multiple products.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If custom ASICs are used for processing neural networks, then processing efficiency is improved, but design time and non-recurring engineering costs increase
Solution Approach 1:
The system segments the neural network processing into multiple identical dies, where each die handles a specific layer or set of layers. This segmentation allows parallel processing of different neural network layers while using standardized die designs, thereby improving processing efficiency without requiring custom design for each layer.
Solution Approach 2:
The patent employs universal interconnect structures that can be configured through programming to handle different neural network architectures and layer configurations. The same physical interconnect hardware serves multiple functions by being reprogrammed, eliminating the need for custom ASIC design for each neural network variant.
2Productivity
If custom ASICs are used for processing neural networks, then processing efficiency is improved, but non-recurring engineering costs increase
Solution Approach 1:
The system divides the neural network processing into multiple identical dies, each manufactured using the same standardized process. This segmentation enables volume production of identical units at lower per-unit cost, reducing non-recurring engineering costs while maintaining high processing efficiency through parallel execution.
Solution Approach 2:
The patent creates multiple copies of the same die design to process different layers of the neural network. By copying a single validated die design rather than creating custom ASICs for each layer, the system reduces engineering costs while achieving the parallel processing performance of custom ASICs.
3Loss of time
If multiple identical dies are used, then design time is reduced through reusability, but inter-die communication complexity increases
Solution Approach 1:
The interconnect structures are designed as universal, reconfigurable pathways that can be programmed to connect any die to any other die in various configurations. This universality simplifies the overall design by using a single interconnect template repeatedly, while the programmable nature handles the complexity of different communication patterns.
Solution Approach 2:
The inter-die communication paths are dynamically configurable through programming rather than being fixed physically. This allows the same physical infrastructure to adapt to different neural network architectures and layer configurations, reducing design time while managing communication complexity through software control.
4Ease of manufacture
If multiple identical dies are used, then engineering costs are amortized across products, but manufacturing precision requirements increase
Solution Approach 1:
The standardized die design with universal interconnect interfaces enables the same manufacturing process to be used across multiple products, amortizing engineering costs. The universal interface design incorporates tolerance compensation mechanisms that maintain connection precision without requiring ultra-precise manufacturing.
Data Source
AI summary
Apparatus and methods for processing neural network models are provided. The apparatus can comprise a plurality of identical artificial intelligence processing dies. Each artificial intelligence processing die among the plurality of identical artificial intelligence processing dies can include at least one inter-die input block and at least one inter-die output block. Each artificial intelligence processing die among the plurality of identical artificial intelligence processing dies is communicatively coupled to another artificial intelligence processing die among the plurality of identical artificial intelligence processing dies by way of one or more communication paths from the at least one inter-die output block of the artificial intelligence processing die to the at least one inter-die input block of the artificial intelligence processing die. Each artificial intelligence processing die among the plurality of identical artificial intelligence processing dies corresponds to at least one layer of a neural network.