Neural Network Engine With Analog Differential MAC Units
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Solution Overview
Problem
Current digital signal processing technologies face challenges in implementing efficient and high-throughput neural network operations on integrated circuits, particularly in terms of latency, power consumption, and data transfer efficiency, due to limitations in data serialization, chip-to-chip data transfer, and analog-digital conversion.
Innovation Solution
The implementation of a semiconductor-die-integrated neural network engine with signed multiply-accumulate units that generate dual analog result signals, allowing for differential combination of positive and negative product accumulations, and the use of on-chip storage and through-silicon vias for low-latency data transfer, along with hybrid analog-digital MAC units that execute current-mode operations without digitization between neural nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is transferred between chips using conventional methods, then device connectivity is achieved, but latency increases and bandwidth is limited
Solution Approach 1:
The patent merges multiple neural network layers onto a single semiconductor die, eliminating the need for chip-to-chip data transfer. This integration allows direct communication between layers through on-die interconnects, achieving high-bandwidth operation with low latency while maintaining full connectivity between all neural nodes across layers.
Solution Approach 2:
The patent utilizes three-dimensional stacking with through-silicon vias to create vertical interconnects between memory and processing layers. This third-dimensional approach enables simultaneous data access from multiple memory banks by different neural network layers, dramatically increasing data transfer bandwidth and reducing access latency compared to planar arrangements.
2Measurement precision
If analog-to-digital conversion is performed at each neural node, then signal precision is improved, but power consumption and device complexity increase
Solution Approach 1:
The patent extracts the analog-to-digital conversion function from individual neural nodes and consolidates it into shared converter resources located in buffer regions. Multiple neural nodes share access to these conversion resources through time-multiplexed operation, maintaining signal precision while dramatically reducing the total power consumption and device complexity compared to having dedicated converters at each node.
Solution Approach 2:
The shared analog-to-digital converters serve multiple neural network layers and numerous neural nodes simultaneously through time-multiplexed operation. This universal conversion resource performs the same function for different nodes at different times, eliminating redundant conversion circuits and reducing overall system power consumption while maintaining full precision capability.
3Productivity
If more neural network layers are integrated, then processing capability increases, but data transfer bottlenecks worsen
Solution Approach 1:
The patent employs three-dimensional stacking with through-silicon vias to create vertical data pathways between memory and processing layers. This 3D architecture enables parallel data access from multiple memory banks simultaneously, allowing multiple neural network layers to operate concurrently without data transfer bottlenecks, thus scaling processing capability while maintaining high bandwidth.
Solution Approach 2:
The patent segments memory into multiple independent banks distributed across different layers, with each bank accessible by specific neural network layers through dedicated through-silicon via interconnects. This segmentation allows parallel data retrieval operations, enabling multiple layers to access their required data simultaneously without contending for the same memory resources, thereby supporting increased processing capability.
4Adaptability or versatility
If chip area is increased to accommodate more neural nodes, then network size increases, but manufacturing cost and device complexity increase
Solution Approach 1:
The patent transitions from two-dimensional planar integration to three-dimensional stacking, allowing neural nodes and memory to be arranged in vertical layers. This 3D architecture dramatically increases the effective integration density, enabling large-scale neural networks with thousands of nodes without proportionally increasing the physical chip footprint, thus maintaining manufacturing feasibility while scaling network size.
Solution Approach 2:
The patent implements a hierarchical nested structure where memory banks are integrated within or adjacent to processing layers, with through-silicon vias providing direct vertical access. This nesting approach minimizes interconnect length and complexity by placing frequently accessed data close to the processing units that need it, reducing overall device complexity while supporting large network sizes.
Data Source
AI summary
One or more neural network layers are implemented by respective sets of signed multiply-accumulate units that generate dual analog result signals indicative of positive and negative product accumulations, respectively. The two analog result signals and thus the positive and negative product accumulations are differentially combined to produce a merged analog output signal that constitutes the output of a neural node within the subject neural network layer.