Contextual Embedding IC Architecture for Low-Power Edge Inference
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing neural networks for AI applications, such as DNNs, face challenges with high computation costs, power consumption, latency, security, and connectivity issues, particularly in edge devices and IoT applications, due to reliance on GPUs, NPUs, and cloud-based solutions, which are inefficient in handling these challenges, particularly in environments where real-time processing and power efficiency are critical for real-time processing and power efficiency.
Innovation Solution
Embedding a contextual embedding model and its weights directly onto an IC device, such as a die or chip, which includes specialized hardware units for neural network operations, reducing the need for data movement and enabling efficient processing, eliminating the need for data transfer and enhancing scalability and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are deployed on GPUs or cloud-based solutions, then high accuracy can be achieved, but computation cost and power consumption increase significantly
Solution Approach 1:
The patent merges the neural network model weights directly into the hardware circuit structure of the IC device. The weight storage elements are integrated within the same chip as the processing units, eliminating the need for external memory access and reducing power consumption while maintaining high accuracy through the embedded architecture.
Solution Approach 2:
The patent creates a hardware copy of the neural network model directly embedded in the IC device. Instead of running software-based DNNs on general-purpose processors, the model is replicated as dedicated hardware circuits with built-in weight storage, enabling efficient inference with lower power consumption.
2Adaptability or versatility
If data is transferred between memory and processing units, then model flexibility is maintained, but latency and processing time increase
Solution Approach 1:
The patent combines weight storage and processing units into a single integrated IC device. The weight storage elements are positioned within the same hardware block as the computation units, enabling direct access without data transfer delays and significantly reducing processing time while maintaining model flexibility through configurable architecture.
3Adaptability or versatility
If neural networks are implemented as software on general-purpose hardware, then adaptability is maintained, but computation efficiency decreases
Solution Approach 1:
The patent segments the neural network functionality into dedicated hardware modules with specialized weight storage units. Each module is optimized for specific computation patterns, enabling high computational efficiency while maintaining adaptability through the ability to configure different network architectures on the same hardware platform.
Solution Approach 2:
The patent creates dedicated hardware copies of neural network operations rather than using software emulation. The weight storage and computation units are replicated as physical hardware circuits, providing computational efficiency comparable to ASICs while maintaining the flexibility to implement different models through hardware reconfiguration.
4Device complexity
If model weights are stored externally, then hardware complexity is reduced, but data movement requirements increase
Solution Approach 1:
The patent merges weight storage and processing functionality into a single IC device. The weight storage elements are integrated within the same hardware block as the computation units, eliminating data movement across memory boundaries and reducing energy consumption while the overall hardware complexity remains manageable through modular design.
Data Source
AI summary
An integrated circuit (IC) device may implement a contextual embedding model. The IC device may include a tokenizer unit, embedder unit, layer normalizer unit, dot unit, activator units, and flow control unit. The tokenizer unit may implement a tokenizer in the model and convert text to tokens using the vocabulary of the model. The embedder unit may implement embedders in the model and generate embeddings from the tokens. The layer normalizer unit may implement one or more layer normalizers in the model and compute embedding vectors. The dot unit may implement matrix multiplication and add operations in the encoders and pooler of the model. The activator units may implement activation functions, including tanh function, in the model. The flow control unit may orchestrate the other components of the IC device based on a timing sequence of neural network operations in the model.