Bayesian Compute Unit Reconfigurable Sampler Weights
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Solution Overview
Problem
Traditional neural networks face issues with overfitting and sensitivity to malicious attacks due to the lack of uncertainty information in training data, leading to inefficiencies and reliability concerns.
Innovation Solution
The implementation of a Bayesian compute unit with a reconfigurable sampler that efficiently generates multiple weights from a single probability distribution, reducing sampling overhead and enabling local processing within compute units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional neural networks use fixed weights without uncertainty information, then the model structure is simple and training is efficient, but the model suffers from overfitting and sensitivity to malicious attacks
Solution Approach 1:
The patent transforms fixed weight parameters into probabilistic parameters with uncertainty information. Each weight is represented as a probability distribution characterized by mean and variance, allowing the model to capture uncertainty and improve reliability while maintaining computational tractability through efficient sampling methods
Solution Approach 2:
The patent segments the weight representation into distinct components: mean values and variance values. This segmentation allows independent optimization and sampling, where the mean captures the primary weight value and the variance captures uncertainty, enabling the model to be more reliable without excessive complexity
2Reliability
If Bayesian neural networks generate multiple weights from different probability distributions, then uncertainty information is captured improving reliability, but sampling overhead increases and processing efficiency decreases
Solution Approach 1:
The patent performs preliminary action by pre-generating and storing multiple samples from each probability distribution during training. These pre-computed samples are then reused during inference, eliminating the need for repeated sampling operations and significantly improving processing efficiency while maintaining the reliability benefits of uncertainty information
Solution Approach 2:
The patent creates copies of weight samples from the probability distributions and stores them in dedicated memory. Instead of generating new samples during each inference operation, the system copies and reuses pre-generated samples, reducing computational overhead and improving processing efficiency while preserving the uncertainty information
3Measurement precision
If Bayesian neural networks access multiple mean and variance values from system memory for each compute node, then accurate probability distributions are obtained, but bandwidth requirements increase and access time increases
Solution Approach 1:
The patent performs preliminary action by pre-fetching and storing multiple mean and variance values in local memory at each compute node before processing. This allows the compute nodes to access the required distribution parameters locally without repeated trips to system memory, maintaining distribution accuracy while significantly reducing data access time
Solution Approach 2:
The patent introduces local memory at compute nodes as an intermediary between system memory and processing units. This intermediary stores the mean and variance values locally, allowing fast access during inference while reducing the bandwidth burden on the system memory interface and eliminating access time bottlenecks
4Productivity
If Bayesian neural networks distribute different probability distributions to every compute node, then each node can process independently improving parallelism, but bandwidth consumption increases and data movement increases
Solution Approach 1:
The patent applies universality by having compute nodes share common probability distribution parameters (mean and variance values) that are broadcast once to all nodes. Each compute node then independently generates its own weight samples from these shared parameters, enabling parallel processing while minimizing bandwidth consumption since the distribution parameters are transmitted only once rather than individually to each node
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture providing a Bayesian compute unit with reconfigurable sampler and methods and apparatus to operate the same are disclosed. An example apparatus includes a number generator to generate a sequence of numbers; a multiplier to generate a plurality of products by multiplying respective numbers of the sequence of the numbers by a variance value; and an adder to generate a plurality of weights by adding a mean value to the plurality of products, the plurality of weights corresponding to a single probability distribution.