Bit Reversal Parallel Processor Memory Banks
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Solution Overview
Problem
Conventional FFT implementations on processors require significant processing power and memory bandwidth, limiting their ability to perform large DFTs in real-time due to computationally intensive bit-reversal operations, especially in systems that rely heavily on hardware-managed cache hierarchies for intermediate result storage.
Innovation Solution
The technique exploits parallelism in processors to efficiently permute data sets based on bit reversal of indices by using a memory with multiple banks that allow parallel access, where bit-reversed indices are modified to distribute destination locations across different banks, enabling simultaneous writing of multiple data points and utilizing parallel threads for bit-reversal operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional FFT implementations use hardware-managed cache hierarchies for intermediate result storage, then memory management is automated, but processing power and memory bandwidth are significantly consumed
Solution Approach 1:
The patent divides the memory system into multiple independently accessible banks, allowing parallel data access operations. By segmenting the memory into banks that can be accessed simultaneously, the system reduces the processing overhead associated with centralized cache management while maintaining automated memory management capabilities.
Solution Approach 2:
The patent introduces a new dimension of parallelism by organizing memory access patterns across multiple banks simultaneously. Instead of sequential access through a single cache hierarchy, the system accesses multiple memory banks in parallel, effectively adding a dimensional aspect to memory management that reduces processing power requirements.
2Device complexity
If conventional FFT implementations perform bit-reversal operations sequentially, then implementation is simple, but computational intensity increases and processing time extends
Solution Approach 1:
The bit-reversal operation is segmented across multiple parallel processing threads, each handling a portion of the data simultaneously. This division allows the computationally intensive bit-reversal to be performed in parallel, dramatically reducing processing time while maintaining implementation clarity through structured thread organization.
Solution Approach 2:
The patent performs bit-reversal operations as a preliminary step before the main FFT computation, organizing data in the required order beforehand. This preliminary organization enables the subsequent FFT stages to proceed efficiently without repeated data reordering, improving overall processing speed.
3Reliability
If large DFTs are performed to maintain signal quality in real-time systems, then signal quality is preserved, but the processor cannot perform other operations due to limited processing power
Solution Approach 1:
The large DFT computation is segmented into multiple smaller processing units that can execute in parallel across different threads and memory banks. This segmentation allows the system to maintain large transform sizes for signal quality while distributing the computational load, freeing processor resources for other operations.
Solution Approach 2:
The patent implements overlapping computation and data transfer operations, allowing the FFT processing to continue uninterrupted while data is being moved between memory banks. This continuous processing ensures large DFTs are completed within real-time constraints, maintaining signal quality and processor availability.
4Manufacturing precision
If bit-reversal operations are performed on all data points, then correct output sequence is achieved, but memory bandwidth requirements increase
Solution Approach 1:
The memory system is segmented into multiple banks that can be accessed in parallel during bit-reversal operations. By distributing data access across multiple banks simultaneously, the system achieves correct output sequencing without proportionally increasing total memory bandwidth consumption, as parallel accesses occur concurrently rather than sequentially.
Data Source
AI summary
Parallelism in a processor is exploited to permute a data set based on bit reversal of indices associated with data points in the data set. Permuted data can be stored in a memory having entries arranged in banks, where entries in different banks can be accessed in parallel. A destination location in the memory for a particular data point from the data set is determined based on the bit-reversed index associated with that data point. The bit-reversed index can be further modified so that at least some of the destination locations determined by different parallel processes are in different banks, allowing multiple points of the bit-reversed data set to be written in parallel.