Processing in memory (PIM) offers a compelling pathway to overcome the data movement bottleneck in modern AI and data-centric systems. This work introduces MITRA, a reconfigurable magnetic tunnel junction (MTJ)-based in-memory architecture that leverages stochastic computing (SC) to implement a broad class of transcendental and nonlinear functions directly within memory. By combining stochastic bit-stream processing with compact finite-state-machines (FSMs) embedded in MTJ-FinFET logic-in-memory structures, the proposed design achieves low-latency and power-efficient computation without external datapaths, unlike the binary counterparts. Circuit-level simulations in 14-nm FinFET technology verify correct state transitions, stable stochastic outputs, and predictable power profiles. Extensive evaluations demonstrate high accuracy even with short bit-streams. We further integrate the design into a neural-network classifier and develop an FSM-aware training strategy that compensates for approximation errors, achieving up to 96.9% classification accuracy on the UCI Optical Digit benchmark. Overall, MITRA provides a compact, reconfigurable platform for nonlinear processing in next-generation edge AI systems.
Farzad Razi, M. Moghadam, M. Najafi et al.· Proceedings of the ACM/IEEE...· 0 citations
This study introduces an effective optimization technique for identifying the optimal hyperparameters of a deep neural network (DNN) designed to model the behavior of power amplifiers (PAs). Hence, an innovative and efficient yield-analysis-based approach is proposed to improve both the modeling accuracy and the digital predistortion (DPD) performance of PAs. The method leverages a long short-term memory (LSTM) DNN architecture to capture the nonlinear and memory effects inherent in PA systems, thereby enhancing overall performance and linearization capability. To achieve optimal training, multiple optimization strategies are systematically applied to determine the most suitable hyperparameters, such as learning rate, network depth, and neuron configuration. To validate the effectiveness of the proposed method, a PA operating in the 1.8 GHz to 2.2 GHz frequency range is considered, for which extensive simulations and evaluations demonstrate that the optimized DNN achieves minimal modeling error while significantly improving DPD performance. The results confirm that the proposed framework offers a reliable and efficient solution for PA modeling and linearization, outperforming conventional techniques in terms of accuracy and consistency.
Lida Kouhalvandi, L. Matekovits, Sercan Aygün et al.· Signal Processing and Commun...· 0 citations