Armin Gerami

🔬 Research

Fastmax: Extensively Optimized Linear Attention Implementation
Armin Gerami, Ramani Duraiswami; Supercomputing'25
Summary: Linear attention (Kernel Separation) is a promising approach for calculating the attention in transformers with linear time complexity. However, it introduces a significant data movement overhead. We present a CUDA library that achieves a 3.3× speedup and 3.6× reduction in memory consumption for linear attention implementation compared to the state-of-the-art.

GUST: Graph Edge-Coloring Utilization for Accelerating Sparse Matrix Vector Multiplication
Armin Gerami, Bahar Asgari; ASPLOS'25
Summary: A software/hardware codesign to accelerate sparse matrix-vector multiplication. The harware enables resource sharing while the graph-edge-coloring schedules the input stream to prevent collisions.

Efficient Spatial Audio Rendering Via Differentiable FIR To IIR Estimation
Armin Gerami, Bowen Zhi, Dmitry N. Zotkin, Ramani Duraiswami; ICASSP'25
Summary: We introduce a novel convex optimization algorithm for estimating FIR filters with an IIR filter, and achieve 2× speedup with an accuracy of %0.01 through differential programming.

Room Impulse Response Synthesis via Differentiable Feedback Delay Networks
Armin Gerami, Ramani Duraiswami; WASPAA'25
Summary: We introduce a computationally efficient and tunable feedback-delay-network (FDN) architecture for real-time room-impulse-response (RIR) rendering, addressing the computational and latency challenges inherent in traditional convolution and Fourier transform-based methods.

🚀 Invention Disclosures

Differentiable FIR To IIR Filter Estimation

Rapid Energy and Emission Auditor

💻 Coding Languages

CUDA, C++, Python, Verilog