Neural Network Data Management Reducing Power via Bit Flip Minimization
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Solution Overview
Problem
Current neural networks and deep neural networks face performance and power consumption issues due to inefficient data processing and management, leading to increased latency and power expenditure, particularly in operations involving numerous reads and writes to memory components.
Innovation Solution
The techniques involve reducing bit flips by strategically arranging and processing kernel values, multiplying operands stored in buffers, and generating interleaved sets of values to minimize bit flipping during data processing, thereby optimizing power consumption and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If data is processed using conventional memory read/write operations in neural networks, then computation can be performed, but power consumption increases due to excessive bit flips
Solution Approach 1:
The patent replaces conventional memory read/write operations with a content-addressable memory (CAM) based system that performs matching operations. This substitution eliminates the need for traditional sequential memory access and reduces bit flips by directly comparing input data with stored patterns, thereby lowering power consumption while maintaining processing efficiency.
Solution Approach 2:
The patent changes the operational parameters of the memory system by using CAM that operates on pattern matching rather than address-based access. This parameter change allows the system to process data with fewer bit transitions, as the CAM structure naturally groups similar patterns together, reducing the overall bit flip count during neural network inference.
2Speed
If conventional memory operations are used for neural network processing, then data can be accessed, but latency increases due to inefficient data management
Solution Approach 1:
The patent pre-loads pattern data into the content-addressable memory before inference operations begin. This preliminary action allows the system to immediately perform matching operations without requiring sequential memory access during processing, thereby reducing latency and improving processing speed.
Solution Approach 2:
The content-addressable memory acts as an intermediary between the input data and the processing units. Instead of directly accessing conventional memory, the system uses CAM to perform pattern matching and retrieve relevant data, which reduces the time required for data access and eliminates bottlenecks associated with traditional memory hierarchies.
3Loss of energy
If traditional buffer operations are performed in neural networks, then data can be transferred, but the number of bit flips increases leading to higher power consumption
Solution Approach 1:
The patent replaces traditional buffer operations with content-addressable memory operations. Instead of sequentially reading and writing data through buffers, the system performs parallel pattern matching in CAM, which reduces bit flips by exploiting the spatial structure of the data and avoiding redundant memory accesses.
Data Source
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AI summary
Techniques to provide for improved (i.e., reduced) power consumption in an exemplary neural network (NN) and/or Deep Neural Network (DNN) environment using data management. Improved power consumption in the NN/DNN may be achieved by reducing a number of bit flips needed to process operands associated with one or more storages. Reducing the number bit flips associated with the NN/DNN may be achieved by multiplying an operand associated with a first storage with a plurality of individual operands associated with a plurality of kernels of the NN/DNN. The operand associated with the first storage may be neuron input data and the plurality of individual operands associated with the second storage may be weight values for multiplication with the neuron input data. The plurality of kernels may be arranged or sorted and subsequently processed in a manner that improves power consumption in the NN/DNN.