Switched-Capacitor Vector-Matrix Multiplier With Parallel Analog MAC
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Solution Overview
Problem
Matrix multiplication, a critical operation in machine learning and linear algebra, is computationally expensive and power inefficient due to serial computations and limited memory bandwidth in digital domain, especially for large matrices.
Innovation Solution
A switched capacitor matrix multiplier using successive approximation registers (SAR) operates in parallel, performing bit-wise multiplication and accumulation in the analog domain, eliminating the need for high bandwidth memory and enabling local storage of weight values, thus achieving power efficiency and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If serial computations are used in digital domain, then implementation simplicity is maintained, but throughput and power efficiency deteriorate
Solution Approach 1:
The patent replaces digital computational operations with analog electrical operations. Specifically, it uses analog multiply-and-accumulate (MAC) circuits that perform matrix multiplication through continuous voltage signals and capacitive accumulation, substituting the discrete digital computation process with a continuous analog physical process that inherently performs parallel operations.
Solution Approach 2:
The patent transitions from one-dimensional serial processing to multi-dimensional parallel processing by organizing multiple MAC circuits in a matrix array configuration. Each MAC circuit handles a specific element of the matrix multiplication, allowing simultaneous computation of multiple output elements through spatial parallelism in the analog domain.
2Speed
If high bandwidth memory is used, then computation speed is improved, but power consumption and system complexity increase
Solution Approach 1:
The patent extracts the weight matrix data from external high-bandwidth memory and stores it locally within the analog computing array. Each MAC circuit or group of circuits has direct access to its required weight values through local capacitive storage, eliminating the need for continuous high-speed data transfer from external memory and the associated power consumption.
Solution Approach 2:
The patent performs preliminary loading of weight matrix values into local capacitive storage elements before the actual computation begins. This pre-positioning of data in the analog domain allows the computation to proceed without repeated memory access, as all required weight values are already available at the computational nodes in their analog form.
3Use of energy by moving object
If digital domain computation is used, then precision is maintained, but power efficiency and scalability deteriorate
Solution Approach 1:
The patent changes the fundamental parameter domain from digital discrete values to analog continuous voltages. By representing computational data as analog voltage levels and performing operations through analog circuit physics (capacitive charging, resistive division), the system achieves power-efficient computation while maintaining sufficient precision through careful design of the analog MAC circuits and successive approximation registers.
Solution Approach 2:
The patent introduces successive approximation registers (SAR) as intermediary components that bridge the analog computation domain and digital output domain. The SAR circuits convert the analog accumulation results back to digital values with controlled precision, serving as an interface that allows the system to benefit from analog power efficiency while delivering digital precision outputs.
4Productivity
If parallel processing is implemented, then throughput is increased, but device complexity and area increase
Solution Approach 1:
The patent merges multiple computational functions into unified analog MAC circuit blocks. Each MAC circuit simultaneously performs multiplication and accumulation operations that would require separate digital units, reducing the overall device area. The analog nature of the circuits allows multiple operations to share common physical resources such as capacitors, resistors, and interconnects.
Solution Approach 2:
The patent replaces area-intensive digital logic circuits with compact analog electrical circuits. The analog MAC circuits use passive components (capacitors, resistors) and simple active elements that occupy significantly less silicon area than equivalent digital computational units, enabling high parallelism within a compact footprint.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces power consumption and increases throughput by performing massively parallel multiply-and-accumulate operations with low latency, allowing for efficient computation of large matrix products and enabling the construction of large neural networks with improved power efficiency.
Implementation Method 1
the bit-wise multiplication of a digital weight matrix with a digital input vector is performed by the successive approximation registers (SAR) using only the successive approximation register (SAR) capacitors
Data Source
AI summary
Methods and apparatuses enable a general-purpose low power analog vector-matrix multiplier. A switched capacitor matrix multiplier may comprise a plurality of successive approximate registers (SAR) operating in parallel, each SAR having a SAR digital output; and a plurality of Analog Multiply-and-Accumulate (MAC) units for multiplying and accumulating and scaling bit-wise products of a digital weight matrix with a digital input vector, wherein each MAC unit is connected in series to a SAR of the plurality of SARs.