The article discusses the challenges and opportunities of reducing data movement in modern computing systems through Processing-in-Memory (PIM). Current systems suffer from a significant bottleneck due to the high energy and latency associated with data movement between computation units and memory. To address this, the paper explores two new approaches to enabling PIM: 1) using the analog properties of DRAM to perform massively-parallel operations in memory, and 2) leveraging 3D-stacked memory technology to provide high bandwidth to in-memory logic. The first approach, Ambit, uses the existing DRAM technology to perform bulk bitwise operations, significantly improving performance and energy efficiency. The second approach, Tesserat, uses 3D-stacked memory to enable graph processing with minimal data movement. The paper also discusses the challenges of adopting PIM, including programming models, runtime systems, coherence between PIM logic and CPU cores, and virtual memory support. The authors argue that PIM has the potential to revolutionize data-intensive computing by reducing data movement and improving energy efficiency. The study highlights the importance of continued research into PIM and other data-centric computing paradigms to address the growing demand for efficient data processing in modern systems.The article discusses the challenges and opportunities of reducing data movement in modern computing systems through Processing-in-Memory (PIM). Current systems suffer from a significant bottleneck due to the high energy and latency associated with data movement between computation units and memory. To address this, the paper explores two new approaches to enabling PIM: 1) using the analog properties of DRAM to perform massively-parallel operations in memory, and 2) leveraging 3D-stacked memory technology to provide high bandwidth to in-memory logic. The first approach, Ambit, uses the existing DRAM technology to perform bulk bitwise operations, significantly improving performance and energy efficiency. The second approach, Tesserat, uses 3D-stacked memory to enable graph processing with minimal data movement. The paper also discusses the challenges of adopting PIM, including programming models, runtime systems, coherence between PIM logic and CPU cores, and virtual memory support. The authors argue that PIM has the potential to revolutionize data-intensive computing by reducing data movement and improving energy efficiency. The study highlights the importance of continued research into PIM and other data-centric computing paradigms to address the growing demand for efficient data processing in modern systems.