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Artificial Intelligence (AI) has permeated various domains but is limited by the bottlenecks imposed by data transfer latency inherent in contemporary memory technologies. Matrix multiplication, crucial for neural network training and inference, can be significantly expedited with a complexity of O(1) using Resistive RAM (RRAM) technology, instead of the conventional complexity of O(n2). This positions RRAM as a promising candidate for the efficient hardware implementation of machine learning and neural networks through in-memory computation. However, RRAM manufacturing technology remains in its infancy, rendering it susceptible to soft errors, potentially compromising neural network accuracy and reliability. In this paper, we propose a syndrome-based error correction scheme that employs selective weighted checksums to correct double adjacent column errors in RRAM. The error correction is done on the output of the matrix multiplication thus ensuring correct operation for any number of errors in two adjacent columns. The proposed codes have low redundancy and low decoding latency, making it suitable for high throughput applications. This schemeuses a repeating weight based structure that makes it scalable to large RRAM matrix sizes. 

In integrated circuit design, analysis of wafer map patterns is critical to enhance yield and detect manufacturing issues. With the emergence of novel wafer map patterns, there is increasing need for robust artificial intelligence models that can both accurately classify seen patterns and while also detecting ones not seen during training, a capability known as open world classification. We develop a novel solution to this problem: WaferCap, a Deep Capsule Network designed for wafer map pattern classification and equipped with a rejection mechanism. When evaluated using the WM-811k dataset, WaferCap significantly surpasses existing methods, achieving 99\% accuracy for fully seen patterns while demonstrating robust performance in open-world settings by effectively detecting unseen wafer map patterns. 

Applications involving machine learning and neural networks have become increasingly essential in the AI revolution. Emerging trends in Resistive RAM technologies provide high-speed, low-cost, scalable solutions for such applications. These RRAM cells provide efficient and sophisticated memory hardware structures for machine-learning applications. However, it is difficult to achieve reliable multilevel cell storage capacity in these memory technologies due to the occurrence of soft and hard errors. As these memories can store multi-bits per cell, exploring limited magnitude symbols(multi-bit) error correction in RRAM is important. This paper proposes a new syndrome-based double error correcting code that divides the syndromes into groups and, uses addition and XOR operations to correct double limited magnitude errors in the RRAM cells. The key idea is to use the built-in current summing capability of RRAM cells to perform the addition operations that are used for the error correction thereby greatly reducing the overhead of the decoding logic needed to implement the ECC. This effectively avoids the need for explicit adder hardware in the decoding logic making it smaller and faster than conventional ECC codes with similar error-correcting capability. Experimental results show that the proposed code reduces the number of check symbols and significantly reduces the decoder area and power by using the RRAM cells to perform the addition.