DSP Slice Operand Forwarding for ML Processing Speed
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Solution Overview
Problem
Existing digital signal processing (DSP) architectures, such as those in FPGAs, are inefficient in processing less complex operands common to machine learning applications, as they often require processing all operands equally, leading to suboptimal processing speed and efficiency.
Innovation Solution
Implementing a DSP architecture that allows for the forwarding of a common operand between DSP slices, enabling parallel computation of branches in a decision tree, thereby optimizing processing by skipping unnecessary operations and focusing on frequently used operands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If DSP architectures process all operands equally through sequential retrieval and processing, then hardware implementation simplicity is maintained, but processing speed and efficiency deteriorate for machine learning applications with common operands
Solution Approach 1:
The patent applies preliminary action by pre-forwarding common operands from one DSP slice to another before they are needed for processing. This allows the receiving DSP slice to immediately use the forwarded operand without sequential retrieval, thereby improving processing speed for machine learning workloads that frequently use common operands across multiple computations
Solution Approach 2:
The patent introduces an intermediary mechanism where one DSP slice acts as a forwarder for common operands to other DSP slices. This intermediary approach enables efficient sharing of operands across the DSP fabric without requiring complex centralized memory management, balancing the trade-off between processing efficiency and hardware complexity
2Productivity
If DSP slices process complex operands with full precision, then computation accuracy is maintained, but processing efficiency deteriorates for machine learning applications that use less complex operands
Solution Approach 1:
The patent applies local quality by allowing different DSP slices to handle operands with different precision requirements based on local needs. Machine learning DSP slices can efficiently process less complex operands (e.g., 1, 2, or 4 bits) while other DSP slices maintain full precision processing capabilities, optimizing overall system efficiency without sacrificing necessary accuracy
3Loss of time
If all operands are processed sequentially through a single processing thread, then hardware resource usage is simplified, but processing time increases for operations involving common operands used in multiple computations
Solution Approach 1:
The patent reduces processing time by pre-forwarding common operands to the destination DSP slice before the computation is initiated. This eliminates the need for sequential retrieval during processing, allowing multiple computations sharing common operands to proceed in parallel without increasing overall processing thread complexity
Solution Approach 2:
The patent implements skipping by allowing DSP slices to bypass the sequential operand retrieval step when operands have been previously forwarded. This enables the system to rush through the processing of common operands by directly using the pre-positioned data, significantly reducing processing time for machine learning workloads with repeated operand usage
Data Source
AI summary
Apparatuses and methods are disclosed for an FPGA architecture that may improve processing speed and efficiency in processing less complex operands. Some applications may utilize operands that are less complex, such as operands that are 1, 2, or 4 bits, for example. In some examples, the DSP architecture may skip or avoid processing all received operands or may process a common operand more frequently than other operands. An example apparatus may include configurable logic blocks including DSP slices and an interconnected coupling the configurable logic blocks. An operand register of a DSP slice may include an operand input that is coupled to an output of that DSP slice.