17 citations · 20 across the 11 of their papers we have counts for
11 papers
From Patterns to Parsers: Automatic Generation of Efficient Hardware Parsers for FPGAs
Tushar Garg, Andrew Boutros
This work presents an open-source tool for automatically generating efficient hardware parsers from high-level specifications. It uses a parsing intermediate representation (PIR) t…
Jack of All Scales: A Versatile FPGA Tensor Block for MXFP Precisions
Marwan Mekhemer, Ahmed Elsousy, Balaji Venkatesh +4
Modern deep learning workloads increasingly rely on narrow numerical formats to improve efficiency and reduce memory footprint. The recently standardized microscaling floating-poin…
Boosting FPGA Performance with Direct BRAM-DSP Paths
Jiajun Hu, Ruthwik Reddy Sunketa, Andrew Boutros +1
Efficient data movement between memory and compute units is a key performance bottleneck in modern FPGA designs, particularly for deep learning (DL) workloads. In typical FPGA arch…
Modeling, Optimizing and Exploring Multi-Die FPGA Routing Architectures
Amirhossein Poolad, Soheil Gholami Shahrouz, Andrew Boutros +1
Die stacking has enabled 2.5D FPGAs by integrating multiple active dice on a passive silicon interposer for improved yield and capacity, and paved the way for 3D architectures that…
Déjà Vu Packing: Optimizing FPGA Logic Clustering Runtime via Pattern Memoization
Milo Liebster, Amin Mohaghegh, Andrew Boutros
Implementing a digital circuit on an FPGA fabric requires clustering technology-mapped netlist primitives into coarser-granularity blocks that can be directly mapped to the physica…
Double Duty: FPGA Architecture to Enable Concurrent LUT and Adder Chain Usage
Junius Pun, Xilai Dai, Grace Zgheib +4
Flexibility and customization are key strengths of Field-Programmable Gate Arrays (FPGAs) when compared to other computing devices. For instance, FPGAs can efficiently implement ar…
Field-Programmable Gate Array Architecture for Deep Learning: Survey & Future Directions
Andrew Boutros, Aman Arora, Vaughn Betz
Deep learning (DL) is becoming the cornerstone of numerous applications both in datacenters and at the edge. Specialized hardware is often necessary to meet the performance require…
RAD-Sim: Rapid Architecture Exploration for Novel Reconfigurable Acceleration Devices
Andrew Boutros, Eriko Nurvitadhi, Vaughn Betz
With the continued growth in field-programmable gate array (FPGA) capacity and their incorporation into new environments such as datacenters, we have witnessed the introduction of…
FPGA-based AI Smart NICs for Scalable Distributed AI Training Systems
Rui Ma, Evangelos Georganas, Alexander Heinecke +2
Rapid advances in artificial intelligence (AI) technology have led to significant accuracy improvements in a myriad of application domains at the cost of larger and more compute-in…
Koios: A Deep Learning Benchmark Suite for FPGA Architecture and CAD Research
Aman Arora, Andrew Boutros, Daniel Rauch +9
With the prevalence of deep learning (DL) in many applications, researchers are investigating different ways of optimizing FPGA architecture and CAD to achieve better quality-of-re…
Neighbors From Hell: Voltage Attacks Against Deep Learning Accelerators on Multi-Tenant FPGAs
Andrew Boutros, Mathew Hall, Nicolas Papernot +1
Field-programmable gate arrays (FPGAs) are becoming widely used accelerators for a myriad of datacenter applications due to their flexibility and energy efficiency. Among these app…