7 citations · 9 across the 5 of their papers we have counts for
5 papers
At-the-Roofline Sparse Tensor Contractions on Vector Processors for Transformer Inference
Bowen Wang, Chi Zhang, Diyou Shen +3
Fine-grained weight pruning and activation sparsification have emerged as effective approaches for reducing the compute and memory cost of inference for Transformer models. In the…
VMXDOTP: A RISC-V Vector ISA Extension for Efficient Microscaling (MX) Format Acceleration
Max Wipfli, Gamze İslamoğlu, Navaneeth Kunhi Purayil +2
Compared to the first generation of deep neural networks, dominated by regular, compute-intensive kernels such as matrix multiplications (MatMuls) and convolutions, modern decoder-…
CMOS 2.0 -- Redefining the Future of Scaling
Moritz Brunion, Navaneeth Kunhi Purayil, Francesco Dell'Atti +5
We propose to revisit the functional scaling paradigm by capitalizing on two recent developments in advanced chip manufacturing, namely 3D wafer bonding and backside processing. Th…
TROOP: At-the-Roofline Performance for Vector Processors on Low Operational Intensity Workloads
Navaneeth Kunhi Purayil, Diyou Shen, Matteo Perotti +1
The fast evolution of Machine Learning (ML) models requires flexible and efficient hardware solutions as hardwired accelerators face rapid obsolescence. Vector processors are fully…
AraXL: A Physically Scalable, Ultra-Wide RISC-V Vector Processor Design for Fast and Efficient Computation on Long Vectors
Navaneeth Kunhi Purayil, Matteo Perotti, Tim Fischer +1
The ever-growing scale of data parallelism in today's HPC and ML applications presents a big challenge for computing architectures' energy efficiency and performance. Vector proces…