1D K-Means Hardware Accelerator With Parallel DP Backtracking
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Solution Overview
Problem
Conventional hardware implementations of k-means clustering algorithms are computationally expensive and slow, making them unsuitable for efficiently solving thousands or millions of instances of the 1-dimensional k-means clustering problem in complex machine learning applications.
Innovation Solution
A hardware accelerator configured to perform k-means clustering on 1-dimensional inputs using a dynamic programming algorithm, which computes a minimum within-cluster sum of squares matrix and backtrack index in parallel, utilizing a two-row ping pong buffer in shared memory and global memory to optimize computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional hardware implementations of k-means clustering algorithms are used, then the algorithm can be executed on standard hardware, but the computation is expensive and slow
Solution Approach 1:
The patent segments the k-means clustering computation into independent 1-dimensional subproblems that can be processed in parallel. Each thread handles a specific portion of the data, dividing the overall computational task into manageable segments that execute simultaneously, thereby reducing total computation time and increasing productivity.
Solution Approach 2:
The patent transforms the traditional multi-dimensional k-means problem into a series of 1-dimensional clustering problems. By sorting the data and processing each dimension independently through dynamic programming, the system achieves parallelization that was not possible in conventional implementations, significantly reducing computational cost while maintaining clustering effectiveness.
2Productivity
If dynamic programming algorithm with parallel processing is implemented, then computational speed-ups are achieved, but device complexity increases
Solution Approach 1:
The hardware accelerator is designed with multi-functionality to handle various stages of the dynamic programming algorithm. The same accelerator processes data sorting, minimum within-cluster sum of squares computation, and backtrack index generation. This universal design reduces the need for separate specialized hardware components, thereby managing device complexity while maintaining high processing efficiency.
Solution Approach 2:
The patent introduces a shared memory buffer as an intermediary between the parallel threads and global memory. This buffer facilitates efficient data exchange and coordination between threads, reducing the complexity of memory management in the parallel system. The intermediary structure simplifies the hardware architecture by providing a standardized interface for data access and synchronization.
3Productivity
If two-row ping pong buffer is used in shared memory, then parallel computation is optimized, but memory management complexity increases
Solution Approach 1:
The patent implements periodic swapping between two rows in the shared memory buffer. As threads complete computation for one row, the buffer pointers are periodically switched to the other row, enabling continuous parallel processing without waiting for memory operations to complete. This periodic action optimizes throughput by keeping all threads continuously productive, while the simple swap operation keeps memory management straightforward.
Data Source
AI summary
A hardware accelerator that performs k-means clustering on 1-dimensional inputs by computing a minimum within-cluster sum of squares matrix and a backtracking index, and using the backtracking index to identify start and end points for clusters within the 1-dimensional inputs. The within-cluster sum of squares matrix is generated in parallel by differing threads, using a two row ping pong buffer in shared memory of the thread block. The 1-dimensional inputs are read into shared memory and accessed as the threads compute successive rows of the minimum within-cluster sum of squares matrix. The backtrack index is stored in global memory and holds index values for the 1-dimensional inputs that minimize the minimum within-cluster sum of squares function at each element in the sum of squares matrix. After identifying the start and end points for the clusters, cluster labels can be generated for each of the 1-dimensional inputs.