Sparse General matrix multiplication (SpGEMM) is a fundamental kernel in many scientific and engineering fields, including Artificial Intelligence (AI). However, its intrinsic computation complexity presents substantial challenges, making efficient hardware implementation particularly difficult. This paper proposes SPARCAM, a novel SpGEMM accelerator, developed and optimized for very energy-efficient AI edge applications. SPARCAM is designed using low-power dense Gain Cell embedded DRAM (GC-eDRAM) technology, a processing near memory paradigm, and a modified outer product matrix multiplication algorithm. Despite its quite limited peak theoretical performance, SPARCAM achieves very high energy efficiency due to its low-power architecture and almost 100% utilization of its computing resources. Designed in a commercial 28 nm FDSOI technology, SPARCAM achieves 13 . 9 × speedup over a high-performance embedded CPU when processing large-scale sparse matrices. When multiplying limited-size sparse matrices, SPARCAM obtains 193 × speedup over high-performance GPU. SPARCAM reaches about 4.3 orders-of-magnitude, on average, higher energy benefits, and 1892 × , 181 × , 2 × , and 3471 × , higher energy efficiency (over CPU) compared with state-of-the-art SpGEMM accelerators SpArch, OuterSPACE, MatRaptor, and high-performance GPU, respectively.

This work presents a non-volatile content-addressable memory (NV-CAM) based on double-barrier magnetic tunnel junction technology (DMTJ). Unlike state-of-the-art NV-CAM designs that present low-performance updates, our NV-CAM allows energy-efficient, high-performance search and update operations. This makes it well-suited for applications requiring a high frequency of searches/updates, such as associative processors. The NV-CAM hybrid CMOS/DMTJ was designed using a commercial 65nm CMOS technology and a Verilog-A-based DMTJ compact model. The NV-CAM evaluation was carried out by employing Monte Carlo simulations while accounting for process variations. Simulation results show that our NV-CAM presents competitive figures of merit compared to state-of-the-art design. Our NV-CAM presents energy-efficient operations and reduces the update and search delay by about 71% and 75%, respectively, compared to other NV-CAMs.

Content-addressable memory (CAM) is a specialized memory architecture designed for fast data searches, allowing a one-clock-cycle comparison between the search input and the entire memory content. In this work, a low matchline voltage swing CAM is proposed to reduce the search power consumption while maintaining high-speed search operations. Low voltage swing in the matchline is enabled by introducing extra circuitry in the conventional CAM cell. By means of comprehensive Monte Carlo and post-layout simulations using a commercial 65nm node, we show that the proposed CAM cell design allows for robustness against process, voltage, and temperature variations without the need for dedicated matchline sense schemes. Compared to conventional precharge high NOR-type CAM, the proposed design achieves 42% higher speed and 29.1% less energy consumption. Post-layout results demonstrate that the proposed CAM operates reliably at 0.6V, maintaining performant and reliable search operations across a wide temperature range.