ADC-PIM: Accelerating Convolution on the GPU via In-Memory Approximate Data Comparison

Near-DRAM computing strategies advocate for providing computational capabilities close to where data is stored. Although this paradigm can effectively address the memoryto-processor communication bottleneck, it also presents new challenges: The strict resource constraints in the memory periphery demand careful tailoring of architectural elements. We herein propose a novel framework and methodology to explore Compute-near-Memory designs that interface to DRAM memory banks, demonstrating the area, energy, and performance tradeoffs subject to the architectural configuration. We exemplify this methodology by conducting two studies on compute-nearbank designs: (1) analyzing the interaction between control and data resources, and (2) exploring the integration of processing units with different DRAM standards. According to our study, the optimal size ratios between instruction and data capacity vary from 2× to 4× across benchmarks from representative application domains. The retrieved Pareto-optimal solutions from our framework improve state-of-the-art designs, e.g., achieving a 50% performance increase on matrix operations with 15% energy overhead relative to the FIMDRAM design. In addition, the exploration of DRAM shows the interplay between available internal bandwidth, performance, and area overhead. For example, a three-fold increase in bandwidth rises performance by 47% across workloads at a 34% extra area cost.

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ADC-PIM: Accelerating Convolution on the GPU via In-Memory Approximate Data Comparison

Cited by 3 publications

References 41 publications

Approximate Computing: Concepts, Architectures, Challenges, Applications, and Future Directions

Approximate Computing: Concepts, Architectures, Challenges, Applications, and Future Directions

Accelerating CNN Training With Concurrent Execution of GPU and Processing-in-Memory

Bank on Compute-Near-Memory: Design Space Exploration of Processing-Near-Bank Architectures

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