Adaptive Bit Allocation in Neural Network Synapse Circuits
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Solution Overview
Problem
Neural systems face challenges in optimizing bit allocation for neural signals and parameters to minimize memory usage while maintaining performance metrics such as timing accuracy and learning rate, as existing methods do not effectively adapt to dynamic neural signals and varying sensitivity of synaptic weights.
Innovation Solution
A method and apparatus that allocate quantization levels and bits based on the sensitivity of performance metrics to quantization errors, with systematic dithering of Least Significant Bits (LSBs) to reduce errors and adapt bit allocation dynamically, optimizing bit usage for neural dynamics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If uniform bit allocation is used for all neural signals and parameters, then memory usage is simplified and predictable, but memory space is wasted and precision is insufficient for sensitive parameters
Solution Approach 1:
The patent applies local quality by differentiating bit allocation across different neural signals and parameters based on their individual sensitivity characteristics. Instead of uniform allocation, the system assigns higher bit precision to parameters with higher sensitivity (such as synaptic weights affecting learning rate) and lower bit precision to less sensitive parameters, thereby optimizing memory space utilization while maintaining necessary precision for each component.
2Measurement precision
If higher bit precision is allocated to all neural data, then timing accuracy and performance metrics are maintained, but memory space consumption increases
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the bit precision parameter based on the sensitivity of each neural parameter to quantization errors. The system calculates sensitivity measures for different parameters (such as synaptic weights, neural states, and timing parameters) and modifies the bit allocation parameter accordingly, allocating more bits to parameters where quantization errors would have larger impact on performance metrics like timing accuracy and learning rate.
3Ease of manufacture
If fixed quantization levels are used for all neural signals, then the system is simple to implement, but it cannot adapt to dynamic neural signals and varying sensitivity
Solution Approach 1:
The patent applies dynamics by transitioning from fixed quantization levels to adaptive quantization that changes based on the dynamic characteristics of neural signals and the varying sensitivity of different parameters. The system continuously monitors and adjusts quantization levels according to the current state of neural activity and the sensitivity profiles of different parameters, enabling the system to adapt to dynamic neural signals while maintaining implementation feasibility through automated adjustment mechanisms.
Data Source
AI summary
Certain aspects of the present disclosure support a technique for adaptive bit-allocation in neural systems. Bit-allocation for neural signals and parameters in a neural network described in the present disclosure may comprise for a plurality of synapse circuits in the neural simulator network, dynamically allocating a number of bits to the neural circuit signals based on at least one characteristic of one or more neural potential in the neural simulator network; and for the plurality of synapse circuits in the neural simulator network, dynamically allocating a number of bits to at least one neural processing parameter of the synapse circuit based on at least one condition of the neural simulator network.