This study explores the versatility of 1T-nC ferro-electric RAM (FeRAM) memory capable of operating in both secure storage and highly energy-efficient Compute-in-Memory (CiM) modes without incurring any extra design overhead. In contrast to conventional, volatile 1T-1C Dynamic Random Access Memory (DRAM), having significant energy cost due to the continuous refresh cycles, FeRAM presents a robust non-volatile memory solution, enabling efficient data retention with minimal power consumption. Bitwise logic operations (AND, OR) are efficiently implemented using a single, unmodified 1T-nC FeRAM cell and single-row activation (SRA), while NOT operation is realized by a modified sense amplifier. These facilitate bulk bitwise operations with significant energy benefits compared to multi-row based DRAM solutions. Furthermore, we demonstrate key-based (K) cryptographic operations which help in storing Plain Text (PT) in Cipher Text (CT) format tightly coupled with the memory write/read cycles in secure-mode operation with minimal overload while maintaining robust security. In addition, the advantages of vertical 3D integration of $1 \mathrm{~T}-\mathrm{nC}$ FeRAM are evaluated, and thermal profiling of the remnant polarization ($\mathrm{P}_{\mathrm{R}}$) is done. Furthermore, we demonstrate that 1T-nC FeRAM memory reduces energy consumption by 31% compared to DRAM by assessing 8 real-world data-intensive workloads utilizing bulk bitwise operations.

Video analytics applications are gaining popularity among serverless environments. One issue that appears in existing serverless platforms is that they do not fully exploit opportunities to efficiently handle video chunking as they assume that all video chunks display similar computation and communication overheads. Those overheads vary and benefit from fine-grained handling. Scheduling non-uniform chunks is even more challenging when the hardware resources are heterogeneous and the network between the resources is non-uniform. To address these challenges, we propose SVDE, a heterogeneous serverless cloud framework for massive video processing workloads. SVDE employs a trained decision tree regression to efficiently decide where to process each video chunk, by holistically considering the non-uniform chunk sizes, heterogeneity of the node hardware, queuing status of each node, and an unbalanced network. Furthermore, we develop an efficient operator backend that will be open-sourced as part of SVDE.Compared to prior works, SVDE achieves up to 3.2× speedup on ten real-world video workloads due to its holistic scheduling decision-making, while our operator backend outperforms the popular Pytorch JIT backend by 5×.