32 citations · 71 across the 6 of their papers we have counts for
6 papers
Bitwise Systolic Array Architecture for Runtime-Reconfigurable Multi-precision Quantized Multiplication on Hardware Accelerators
Yuhao Liu, Salim Ullah, Akash Kumar
Neural network accelerators have been widely applied to edge devices for complex tasks like object tracking, image recognition, etc. Previous works have explored the quantization t…
Retrospective: A CORDIC Based Configurable Activation Function for NN Applications
Omkar Kokane, Gopal Raut, Salim Ullah +4
A CORDIC-based configuration for the design of Activation Functions (AF) was previously suggested to accelerate ASIC hardware design for resource-constrained systems by providing f…
AxOMaP: Designing FPGA-based Approximate Arithmetic Operators using Mathematical Programming
Siva Satyendra Sahoo, Salim Ullah, Akash Kumar
With the increasing application of machine learning (ML) algorithms in embedded systems, there is a rising necessity to design low-cost computer arithmetic for these resource-const…
AxOCS: Scaling FPGA-based Approximate Operators using Configuration Supersampling
Siva Satyendra Sahoo, Salim Ullah, Soumyo Bhattacharjee +1
The rising usage of AI and ML-based processing across application domains has exacerbated the need for low-cost ML implementation, specifically for resource-constrained embedded sy…
SIMDive: Approximate SIMD Soft Multiplier-Divider for FPGAs with Tunable Accuracy
Zahra Ebrahimi, Salim Ullah, Akash Kumar
The ever-increasing quest for data-level parallelism and variable precision in ubiquitous multimedia and Deep Neural Network (DNN) applications has motivated the use of Single Inst…
ExPAN(N)D: Exploring Posits for Efficient Artificial Neural Network Design in FPGA-based Systems
Suresh Nambi, Salim Ullah, Aditya Lohana +3
The recent advances in machine learning, in general, and Artificial Neural Networks (ANN), in particular, has made smart embedded systems an attractive option for a larger number o…