Mass Multiplier Circuit for Neural Network Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computer processors, including CPUs, GPUs, and TPUs, are inefficient in performing matrix operations, particularly in neural networks, as they sequentially multiply weights by input values, leading to high computational time and resource usage due to the lack of optimization in the order and sequence of mathematical processes.
Innovation Solution
Implementing a mass multiplier circuit as an integrated circuit that simultaneously multiplies discrete values by a plurality of weight values, allowing for the production of output products through combinatorial logic and pipelining, which reduces the need for RAM buffering and enables parallel processing, thereby increasing throughput and reducing power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional processors sequentially multiply weights by input values, then the computation is performed using standard processing units, but the computational time and resource usage increase significantly
Solution Approach 1:
The patent segments the multiplication operation into multiple parallel processing paths within a single clock cycle. Each weight value has its own multiplication path, allowing simultaneous computation of multiple products rather than sequential processing. This segmentation enables the circuit to compute N products in parallel where N is the number of weight values, directly resolving the contradiction between productivity and time loss.
Solution Approach 2:
The patent transitions from sequential time-based processing to spatial parallel processing by implementing multiple multiplication units that operate simultaneously. The circuit architecture adds a spatial dimension to the computation by arranging multipliers in parallel, allowing all weight-input multiplications to occur at the same time rather than in sequence, thereby increasing throughput while reducing computational time.
2Productivity
If conventional processors use standard sequential multiplication, then the implementation is simple and familiar, but the resource usage and power consumption increase
Solution Approach 1:
The circuit segments the computation into parallel multiplication paths followed by a single reduction adder. Each segment handles one weight-input product, and all segments complete simultaneously in one clock cycle. This segmentation eliminates the need for multiple sequential operation cycles, reducing the total operational time and associated power consumption while maintaining high throughput.
Solution Approach 2:
The patent implements continuous parallel computation where all multiplication operations proceed simultaneously without idle cycles. The circuit maintains continuous useful action by keeping all multiplication units active and productive during each clock cycle, eliminating the wasted time and energy that occurs in sequential processing where most units remain idle during each operation cycle.
3Device complexity
If sequential multiplication is used, then the circuit design is conventional and easier to implement, but the latency increases
Solution Approach 1:
The patent segments the computation into independent parallel multiplication units with a centralized reduction stage. This segmentation allows each multiplication to occur independently and simultaneously, reducing the critical path latency compared to sequential processing. The increased device complexity of having multiple parallel multipliers is offset by the dramatic reduction in computational latency achieved through parallel execution.
Solution Approach 2:
The patent merges all individual multiplication results into a single unified output through a reduction adder tree. This combining operation consolidates the parallel computation results efficiently, allowing the circuit to produce the final sum of all weight-input products in a single clock cycle. The merging strategy reduces latency by eliminating sequential addition operations that would otherwise extend the computational timeline.
4Productivity
If parallel mass multiplication is implemented, then throughput increases significantly, but the circuit complexity increases
Solution Approach 1:
The patent segments the parallel computation into standardized multiplication units and a systematic reduction adder structure. This segmentation provides a modular architecture where complexity is organized and managed through repetition of identical functional blocks, making the design more manageable despite the increased throughput capability. The segmented approach allows systematic scaling while maintaining design regularity.
Solution Approach 2:
The patent implements universal multiplication units that can handle any weight-input pair through the same operational pathway. Each multiplier unit is designed to be universal in function, accepting different weight and input values while performing the same multiplication operation. This universality reduces design complexity by avoiding the need for specialized circuits for different operations, allowing the same hardware structure to be replicated for parallel processing.
Data Source
AI summary
A mass multiplier implemented as an integrated circuit has a port receiving a stream of discrete values and circuitry multiplying each value as received by a plurality of weight values simultaneously. An output channel provides products of the mass multiplier as produced. The mass multiplier is applied to neural network nodes.