We propose a reconfigurable accelerator for parallel workloads called Transmuter with a software stack called TransPy.
We propose a holistic reliability solution for parallel 3D architectures that provides concurrent single-replay detection and diagnosis, fault-mitigating repair and aging-aware lifetime management.
We develop a hardware-software co-designed solution to accelerate sparse matrix-vector multiplication on a TPU-like systolic array architecture.
We prototype a Sparse Matrix-Matrix Multiplication accelerator with 48 heterogeneous cores and a reconfigurable memory hierarchy in 40 nm CMOS that offers significant energy- and bandwidth-efficiency improvements over state-of-the-art CPUs and GPUs.
We architect a novel hardware-software codesigned accelerator for high-performance, energy efficient sparse matrix multiplication targeting graph analytics and scientific computation.