Presentation
MASC: A Memory-Efficient Adjoint Sensitivity Analysis through Compression Using Novel Spatiotemporal Prediction
DescriptionAdjoint sensitivity analysis is critical in modern integrated circuit design and verification, but its computational intensity grows significantly with the size of the circuit, the number of objective functions, and the accumulation of time points. This growth can impede its wider application. The intimate link between the forward integration in transient analysis and the reverse integration in adjoint sensitivity analysis allows for the retention of Jacobian matrices from transient analysis, thereby speeding up sensitivity analysis. However, Jacobian matrices across multiple timesteps are often so large that they cannot be stored in memory during the forward integration process, necessitating disk storage and incurring significant I/O overhead.
To address this, we develop a memory-efficient sensitivity analysis method that utilizes data compression to minimize memory overhead during simulation and enhance analysis efficiency. Our compression method can efficiently compress the sparse tensor that contains the Jacobian matrices over time by exploiting the spatiotemporal characteristics of the data and circuit attributes. It also introduces a shared-indices technique, a cutting-edge spatiotemporal prediction model, and robust residual encoding.
We evaluate our compression method on 7 datasets from real-world simulations and demonstrate that it can reduce the memory requirements for storing Jacobian matrices by more than 16x on average, which is significantly more efficient than other state-of-the-art compression techniques.
To address this, we develop a memory-efficient sensitivity analysis method that utilizes data compression to minimize memory overhead during simulation and enhance analysis efficiency. Our compression method can efficiently compress the sparse tensor that contains the Jacobian matrices over time by exploiting the spatiotemporal characteristics of the data and circuit attributes. It also introduces a shared-indices technique, a cutting-edge spatiotemporal prediction model, and robust residual encoding.
We evaluate our compression method on 7 datasets from real-world simulations and demonstrate that it can reduce the memory requirements for storing Jacobian matrices by more than 16x on average, which is significantly more efficient than other state-of-the-art compression techniques.
Event Type
Research Manuscript
TimeWednesday, June 2610:30am - 10:45am PDT
Location3010, 3rd Floor
EDA
Analog CAD, Simulation, Verification and Test