Analog In-Memory Matrix Multiplication Circuit
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Solution Overview
Problem
Matrix multiplication in AI systems faces significant bandwidth bottlenecks and latency issues due to data transfer between memory and processing units, leading to increased power consumption and inefficiency.
Innovation Solution
Implementing analog in-memory matrix multiplication by performing arithmetic operations directly in memory using analog voltage values, where matrices are stored in transposed form to enable parallel reading and dot product calculations, reducing the need for extensive data transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is transferred between off-chip memory and digital processing unit, then matrix multiplication can be performed, but bandwidth bottleneck increases latency and power consumption
Solution Approach 1:
The patent merges the memory storage function with the processing function by implementing analog in-memory matrix multiplication. The memory array directly performs the multiplication operation through analog voltage representations, eliminating the need for separate data transfer and processing stages. This integration resolves the bandwidth bottleneck by performing calculations where data resides, thereby reducing latency and improving throughput.
Solution Approach 2:
The patent replaces the digital mechanical data transfer and processing system with an analog computational system. Instead of transferring digital data and performing discrete arithmetic operations, the system uses analog voltage values stored in memory cells to directly represent matrix elements and perform continuous analog multiplication, substituting the mechanical digital processing chain with an analog domain approach that eliminates transfer bottlenecks.
2Productivity
If data is transferred between off-chip memory and digital processing unit, then matrix multiplication can be performed, but power consumption increases
Solution Approach 1:
By merging storage and computation into a single in-memory analog system, the patent eliminates repeated data transfers between memory and processing units. This integration reduces the energy consumption associated with data movement and eliminates the need for high-power digital processing, thereby significantly reducing overall power consumption while maintaining computational productivity.
Solution Approach 2:
The patent substitutes the energy-intensive digital data transfer and processing mechanism with an analog computation system that operates directly on voltage representations of data in memory. This analog approach eliminates the need for high-speed digital communication and discrete arithmetic operations, resulting in dramatically reduced power consumption while maintaining computational efficiency.
3Productivity
If matrices are stored in transposed form, then parallel reading and dot product calculations are enabled, but storage arrangement complexity increases
Solution Approach 1:
The patent applies asymmetric storage arrangement by storing matrices in transposed form, where the row-major or column-major ordering is intentionally reversed to enable parallel reading operations. This asymmetric storage pattern allows simultaneous access to multiple elements during analog dot product calculations, creating computational efficiency through parallelism while accepting the resulting complexity in memory access patterns as a trade-off for performance gain.
Data Source
AI summary
Techniques are provided for efficient matrix multiplication using in-memory analog parallel processing, with applications for neural networks and artificial intelligence processors. A methodology implementing the techniques according to an embodiment includes storing two matrices in-memory. The first matrix is stored in transposed form such that the transposed first matrix has the same number of rows as the second matrix. The method further includes reading columns of the matrices from the memory in parallel, using disclosed bit line functional read operations and cross bit line functional read operations, which are employed to generate analog dot products between the columns. Each of the dot products corresponds to an element of the matrix multiplication product of the two matrices. In some embodiments, one of the matrices may be used to store neural network weighting factors, and the other matrix may be used to store input data to be processed by the neural network.