A High-Efficiency Charge-Domain Compute-in-Memory 1F1C Macro Using 2-bit FeFET Cells for DNN Processing by Nellie Laleni, Franz Muller, Gonzalo Cunarro, Thomas Kampfe, Taekwang Jang

Subjects: “…artificial intelligence (AI) accelerator…”
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Heterogeneous Integration Technologies for Artificial Intelligence Applications by Madison Manley, Ashita Victor, Hyunggyu Park, Ankit Kaul, Mohanalingam Kathaperumal, Muhannad S. Bakir

“…HI is becoming increasingly implemented in many AI accelerator products due to its potential to enhance overall system performance while also reducing electrical interconnect delays and energy consumption, which are critical for supporting data-intensive AI workloads. …”
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Evaluation of Novel AI Architectures for Uncertainty Estimation by Erik Pautsch, John Li, Silvio Rizzi, George K. Thiruvathukal, Maria Pantoja

“…Using High-Performance (HPC) platforms, including the traditional Polaris supercomputer and AI accelerators like Cerebras CS-2 and SambaNova DataScale, we assessed the computational merits and bottlenecks of each platform. …”
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Thermal Heating in ReRAM Crossbar Arrays: Challenges and Solutions by Kamilya Smagulova, Mohammed E. Fouda, Ahmed Eltawil

“…The high speed, scalability, and parallelism offered by ReRAM crossbar arrays foster the development of ReRAM-based next-generation AI accelerators. At the same time, the sensitivity of ReRAM to temperature variations decreases <inline-formula> <tex-math notation="LaTeX">$\text{R}_{ON}/\text{R}_{OFF}$ </tex-math></inline-formula> ratio and negatively affects the achieved accuracy and reliability of the hardware. …”
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Co-Optimization of Power Delivery Network Design for 3-D Heterogeneous Integration of RRAM-Based Compute In-Memory Accelerators by Madison Manley, James Read, Ankit Kaul, Shimeng Yu, Muhannad Bakir

“…Three-dimensional heterogeneous integration (3D-HI) offers promising solutions for incorporating substantial embedded memory into cutting-edge analog compute-in-memory (CIM) AI accelerators, addressing the need for on-chip acceleration of large AI models. …”
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