Harmonica: Hybrid Accelerator to Overcome Imperfections of Mixed-signal DNN Accelerators

In recent years, PIM-based mixed-signal accelerators have been proposed as energy- and area-efficient solutions with ultra-high throughput to accelerate DNN computations. However, PIM designs are sensitive to imperfections such as noise, weight/conductance variations, and cell programming errors that substantially degrade the DNN accuracy. To address this issue, we propose a novel algorithm-hardware co-design framework called Harmonica that simultaneously avoids accuracy degradation due to imperfections, improves area utilization and execution time, and reduces energy consumption. Harmonica proposes to select imperfection-sensitive weights using an input channel-wise method and transfer them to a novel and robust digital accelerator while the main computations are performed in the analog PIM cores. Harmonica is adapted to leverage the preceding weight selection method by reducing ADC precision, employing smaller peripheral circuitry, and a hybrid quantization to optimize the design. Our comprehensive experiments show that even in the presence of imperfections as high as 50%, Harmonica reduces the accuracy degradation from 60% - 90% in designs without a protection solution (e.g., in ISAAC or SRE baselines) to 1% - 2% for different DNNs across diverse datasets. In addition, compared to the ISAAC (SRE), Harmonica improves the execution time, energy, area, power, area-efficiency, and power-efficiency by 26% (14%), 52% (40%), 28% (28%), 57% (45%), 43% (7.5×), and 91% (7.3×), respectively. By employing architecture-based differential cells, where two separated categories of crossbars are used for positive and negative weights, Harmonica outperforms ISAAC (SRE) by 75% (9.2×) and 2.65× (10.2×) in terms of area- and power-efficiency.