CROSS-LAYER DESIGN TO ENHANCE THE FUNCTIONALITY AND ROBUSTNESS OF COMPUTING-IN-MEMORY FOR DEEP NEURAL NETWORK ACCELERATORS

<p dir="ltr">Compute-in-memory (CiM) has emerged as a promising solution for edge computing that enables low-latency and energy-efficient computation by mitigating the von Neumann bottleneck. CiM workloads broadly fall into two categories: vector-matrix multiplications (VMMs), which are central to deep neural network (DNN) inference, and in-memory Boolean and arithmetic operations. In VMM CiM, the memory arrays store the weight matrix and the input vectors are applied as wordline signals, enabling in situ computation of dot-products of the weights and the inputs. On the other hand, bit-wise Boolean and arithmetic operations are performed on operands, both of which are stored within the memory.</p><p dir="ltr">While VMM-based CiM accelerators enable efficient DNN execution, they face significant challenges due to crossbar non-idealities and memory faults, which degrade computational accuracy. In contrast, in-memory Boolean and arithmetic CiM offers greater robustness. However, it has its own challenges such as limited range of functions that can be implemented efficiently.</p><p dir="ltr">This dissertation investigates these issues and proposes cross-layer design strategies to enhance the functionality and robustness of CiM accelerators across multiple memory technologies.</p>