Learned Column-Weights for Analog-AI Latency Reduction
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Solution Overview
Problem
Current Analog-AI hardware faces latency issues due to the need for separate computation of data-dependent coefficients like maximum, average, or standard deviation, which delays efficient SIMD multiplication operations in neural networks.
Innovation Solution
Adding additional columns or rows to an Analog-AI tile to encode learned weights that predict these coefficients, allowing their immediate use during forward inference without calculating exact values from the output data vector.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate computation of data-dependent coefficients (maximum, average, standard deviation) is performed before SIMD multiplication, then neural network accuracy is maintained, but latency increases and compute performance decreases
Solution Approach 1:
The patent pre-computes and stores lookup tables containing pre-calculated data-dependent coefficients (maximum, average, standard deviation) for various possible input vectors. During forward inference, instead of computing these coefficients separately for each input vector, the system directly retrieves the pre-computed coefficients from the lookup tables, eliminating the computational delay while maintaining accuracy
Solution Approach 2:
The patent creates approximate copies of the exact coefficient values by using quantized or rounded versions stored in lookup tables. These copied values are sufficient for maintaining neural network accuracy while enabling faster retrieval compared to exact computation, thus reducing latency without significantly compromising precision
2Measurement precision
If separate computation of data-dependent coefficients is performed before SIMD multiplication, then accurate scaling is achieved, but compute performance and energy efficiency deteriorate
Solution Approach 1:
The patent pre-computes scaling coefficients and stores them in lookup tables during an offline phase. During online inference, the system directly retrieves these pre-computed coefficients without performing separate computation, thereby maintaining scaling accuracy while dramatically improving compute performance and energy efficiency
Solution Approach 2:
The patent extracts the computationally intensive coefficient calculation step from the online inference path and moves it to an offline preprocessing phase. The extracted coefficients are then stored for rapid retrieval, separating the heavy computation from the real-time processing to improve overall system productivity
3Loss of time
If additional columns or rows are added to Analog-AI tile to encode learned weights for coefficient prediction, then latency is reduced and compute performance is enhanced, but Analog-AI tile area and energy consumption increase
Solution Approach 1:
The patent designs the additional columns or rows in the Analog-AI tile to serve multiple functions: they store learned weights for predicting data-dependent coefficients, enable rapid coefficient retrieval, and integrate with the existing crossbar array architecture. This multi-functionality reduces the need for separate hardware components, thereby limiting the increase in tile area while achieving reduced latency
Solution Approach 2:
The patent extends the two-dimensional crossbar array architecture by adding additional columns or rows, utilizing the available spatial dimension efficiently. This dimensional extension allows incorporation of coefficient prediction functionality without proportionally increasing overall tile area, as the addition integrates with the existing architectural footprint
4Productivity
If additional columns or rows are added to Analog-AI tile for coefficient prediction, then compute performance is enhanced, but energy consumption of Analog-AI tile increases
Solution Approach 1:
The patent performs coefficient prediction using pre-trained learned weights stored in the additional columns or rows, eliminating the need for separate coefficient computation during inference. This preliminary preparation of prediction weights reduces real-time computational energy requirements, improving compute performance while the energy increase is limited to the overhead of maintaining additional weight storage
Solution Approach 2:
The patent merges the coefficient prediction function with the existing Analog-AI tile multiply-accumulate operations by integrating the additional columns or rows into the same crossbar array fabric. This merging allows the prediction and processing to share hardware resources and energy infrastructure, thereby enhancing compute performance with minimal additional energy consumption compared to separate implementation
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 reduces latency, enhances compute performance, and decreases digital-compute energy while maintaining neural network accuracy with a modest increase in Analog-AI tile energy.
Implementation Method 1
analog conductance states, offering a path towards high-density weight-storage
Implementation Method 2
energy-efficient multiply-accumulate (MAC) operations are performed on area-efficient crossbar-array tiles of analog non-volatile memory
Data Source
AI summary
A method includes receiving, at a neural network weight layer of an artificial neural network, an incoming excitation vector. The artificial neural network includes one or more operations requiring one or more scalar values, such as a mean or a standard deviation, to be computed across an output data vector of the artificial neural network. The method further includes using a predicted representation of the one or more scalar values during forward inference of the artificial neural network by the incoming excitation vector to apply the one or more operations to the output data vector, thus avoiding any computation needed to compute an exact representation of the one or more scalar values from the output data vector.