Chiplet Quantization Blocks Reduce AI Interconnect Bandwidth
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Solution Overview
Problem
Chiplet-based AI architectures face challenges in optimizing AI operations due to complex and large AI workloads, leading to increased data movement and interconnect bandwidth requirements, which strain existing computing resources and hinder performance scaling.
Innovation Solution
A multi-chiplet AI processor with quantization blocks that individually quantize outputs of each chiplet to different bit widths, using statistics from previous input tensors to optimize data representation and reduce memory usage and interconnect bandwidth, while maintaining model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If chiplet-based architectures are used to handle complex AI workloads, then processing capability is improved, but interconnect bandwidth requirements increase
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the precision (bit-width) of data representations in different computational domains. By varying the numerical precision parameter based on the specific computational context and data characteristics, the system reduces interconnect bandwidth requirements while maintaining processing capability. Less precise representations are used for intermediate calculations that tolerate lower precision, while higher precision is reserved for critical operations.
Solution Approach 2:
The patent implements local quality by assigning different precision levels to different chiplets and computational domains based on their specific functional requirements. Rather than uniformly applying high precision across all chiplets, the system tailors the precision level locally to each chiplet's role in the computational graph, optimizing the balance between processing capability and bandwidth consumption.
2Measurement precision
If higher precision data representations are used, then model accuracy is maintained, but memory usage and bandwidth consumption increase
Solution Approach 1:
The patent dynamically changes the precision parameter based on the computational context and data characteristics. By analyzing the distribution and importance of data in different stages of computation, the system adjusts the bit-width representation accordingly, using lower precision where acceptable and higher precision where necessary to maintain model accuracy.
Solution Approach 2:
The patent introduces dynamic precision adjustment mechanisms that adapt the data representation precision during runtime based on observed data characteristics and computational requirements. This dynamic approach allows the system to optimize memory usage and bandwidth consumption while maintaining accuracy where critical, rather than committing to a fixed precision level throughout the computation.
3Ease of operation
If uniform quantization is applied across all chiplets, then implementation simplicity is maintained, but performance optimization is limited
Solution Approach 1:
The patent moves from uniform quantization to localized quantization strategies where each chiplet or computational domain can apply quantization parameters tailored to its specific functional requirements and data characteristics. This allows performance optimization through context-aware quantization while maintaining reasonable implementation complexity through systematic management of the localized parameters.
Solution Approach 2:
The patent segments the quantization strategy into different levels and domains, allowing independent optimization of quantization parameters for different chiplets and computational stages. This segmentation enables performance optimization without requiring complete redesign of the entire system, as each segment can be optimized independently based on its specific requirements.
Data Source
AI summary
A chiplet-based architecture may quantize, or reduce, the number of bits at various stages of the data path in an artificial-intelligence processor. This architecture may leverage the synergy between quantizing multiple dimensions together to greatly decrease the memory usage and data path bandwidth. Internal weights may be quantized statically after a training procedure. Accumulator bits and activation bits may be quantized dynamically during an inference operation. New hardware logic may be configured to quantize the outputs of each operation directly from the core or other processing node before the tensor is stored in memory. Quantization may use a statistic from a previous tensor for a current output tensor, while also calculating a statistic to be used on a subsequent output tensor. In addition to quantizing based on a statistic, bits can be further quantized using a Kth percentile clamping operation.