Joshua Schabel

Papers

Total Citations

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About

Joshua Schabel is a researcher focused on advancing hardware acceleration for artificial neural networks (ANNs), with a particular emphasis on processor-in-memory (PIM) architectures. His most cited work, "Processor-in-memory support for artificial neural networks" (2016, 6 citations), explores how integrating processing capabilities directly into memory can enable real-time, low-power operation for applications such as autonomous vehicles, robotics, recognition, and data mining. This contribution addresses a critical bottleneck in traditional von Neumann architectures—the data movement between memory and processor—by proposing PIM as a solution for efficient ANN execution. While his citation count reflects a niche but growing interest in this domain, Schabel’s work is notable for its forward-looking approach to enabling edge AI and embedded systems. His research underscores the potential of hardware-software co-design to meet the demands of emerging technologies, positioning him as a contributor to the foundational infrastructure for next-generation intelligent systems. For students and researchers, Schabel’s work offers a compelling entry point into the intersection of computer architecture and machine learning, highlighting the importance of specialized hardware in scaling AI.