Clutch: High Performance Vector-Scalar Comparison using DRAM via Chunked Temporal Coding
For memory-bound data-intensive workloads (databases, ML), Clutch provides a high-performance, scalable PuD solution for a fundamental operation.
Clutch accelerates vector-scalar comparisons in Processing-using-DRAM (PuD) systems using chunked temporal coding, achieving 12x throughput and 69x energy efficiency over CPU/GPU, and 2.9x throughput and 3.0x energy efficiency over prior PuD methods.
Vector-scalar comparison is a fundamental computation primitive that compares each element in a vector against a single scalar value. It is widely used in various data-intensive workloads from databases to machine learning. Due to its low computational intensity, its execution tends to be memory-bound, limiting the utilization of compute resources. Processing-using-DRAM (PuD) is an emerging computing paradigm that performs massively parallel bitwise operations directly inside DRAM arrays, alleviating off-chip data movement. Existing PuD-based approaches require many DRAM commands because the comparison's algorithmic complexity grows with operand bit-width in the bit-serial execution model. This command overhead becomes the dominant bottleneck, limiting application-level speedup. We propose Clutch, a data representation and comparison algorithm that accelerates vector-scalar comparisons in PuD systems with high efficiency and scalability. Clutch first uses temporal coding, encoding each vector value as a sequence of leading ones, which enables lookup-based comparison against a scalar by accessing the corresponding DRAM row. To avoid the prohibitive memory footprint of lookup tables at high precision, Clutch partitions operands into multiple multi-bit chunks, compares chunks independently using compact lookup tables, and merges the per-chunk results with a PuD-efficient procedure. By adjusting the number of chunks, Clutch provides a flexible tradeoff between throughput and memory usage. Across predicate evaluation and decision tree inference, Clutch improves end-to-end application throughput and energy efficiency by an average of 12x and 69x over highly optimized CPU and GPU execution, and by 2.9x and 3.0x over the state-of-the-art bit-serial PuD implementation. We also present the first mapping of decision tree inference to PuD execution, extending PuD to a new application domain.