FPGA DSP Block With Tensor Processing for ML and DSP Workloads
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Solution Overview
Problem
Existing integrated circuit devices struggle to efficiently perform calculations required for both machine learning and digital signal processing applications, as the circuitry optimized for one domain is often not well-suited for the other.
Innovation Solution
A digital signal processing (DSP) block is introduced, which includes a tensor processing block capable of performing multiply-accumulate operations efficiently. This block utilizes a virtual bandwidth expansion structure and supports both fixed-point and floating-point arithmetic, allowing it to adapt to various algorithms and precision requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If particular circuitry optimized for digital signal processing is used, then digital signal processing performance is improved, but machine learning performance deteriorates
Solution Approach 1:
The DSP block is designed to perform both digital signal processing operations (multiply-accumulate, addition, subtraction) and machine learning operations (matrix multiplication, convolution) using the same hardware resources. The block can be configured through control signals to switch between different operational modes, making it universally applicable to both domains without requiring separate dedicated circuitry for each function.
2Productivity
If particular circuitry optimized for machine learning is used, then machine learning performance is improved, but digital signal processing performance deteriorates
Solution Approach 1:
The same DSP block that performs machine learning operations also handles digital signal processing tasks. The block's ability to perform arbitrary arithmetic operations (multiply-accumulate, addition, subtraction) and its configurable nature through control signals enable it to efficiently execute both machine learning algorithms and digital signal processing algorithms using identical hardware resources.
3Use of energy by moving object
If fixed-point arithmetic is used, then power consumption is reduced, but precision deteriorates
Solution Approach 1:
The DSP block dynamically switches between fixed-point and floating-point arithmetic modes based on the computational requirements of the algorithm being executed. This dynamic configurability allows the system to use power-efficient fixed-point arithmetic for applications where precision requirements are moderate, while switching to floating-point arithmetic when higher precision is needed, thus optimizing the trade-off between power consumption and precision.
4Measurement precision
If floating-point arithmetic is used, then precision is improved, but power consumption increases
Solution Approach 1:
The system employs dynamic arithmetic mode selection where the DSP block can be configured to use floating-point arithmetic when high precision is required by the algorithm, and switch to fixed-point arithmetic when lower precision suffices. This dynamic adaptation minimizes power consumption by avoiding unnecessary use of power-intensive floating-point operations while maintaining the required precision for each specific computational task.
Data Source
AI summary
The present disclosure describes a digital signal processing (DSP) block that includes a plurality of columns of weight registers and a plurality of inputs configured to receive a first plurality of values and a second plurality of values. The first plurality of values is stored in the plurality of columns of weight registers after being received. Additionally, the DSP block includes a plurality of multipliers configured to simultaneously multiply each value of the first plurality of values by each value of the second plurality of values.