arXiv subjects

Farzad Farshchi

Publications and source records attributed to Farzad Farshchi.

3 recordsLinked to original sources

arXiv2020

New Approximate Multiplier for Low Power Digital Signal Processing

In this paper a low power multiplier is proposed. The proposed multiplier utilizes Broken-Array Multiplier approximation method on the conventional modified Booth multiplier. This method reduces the total power consumption of multiplier up to 58% at the cost of a small decrease in output accuracy. The proposed multiplier is compared with other approximate multipliers in terms of power consumption and accuracy. Furthermore, to have a better evaluation of the proposed multiplier efficiency, it has been used in designing a 30-tap low-pass FIR filter and the power consumption and accuracy are compared with that of a filter with conventional booth multipliers. The simulation results show a 17.1% power reduction at the cost of only 0.4dB decrease in the output SNR.

cs.AR↗

arXiv2019

Integrating NVIDIA Deep Learning Accelerator (NVDLA) with RISC-V SoC on FireSim

NVDLA is an open-source deep neural network (DNN) accelerator which has received a lot of attention by the community since its introduction by Nvidia. It is a full-featured hardware IP and can serve as a good reference for conducting research and development of SoCs with integrated accelerators. However, an expensive FPGA board is required to do experiments with this IP in a real SoC. Moreover, since NVDLA is clocked at a lower frequency on an FPGA, it would be hard to do accurate performance analysis with such a setup. To overcome these limitations, we integrate NVDLA into a real RISC-V SoC on the Amazon cloud FPGA using FireSim, a cycle-exact FPGA-accelerated simulator. We then evaluate the performance of NVDLA by running YOLOv3 object-detection algorithm. Our results show that NVDLA can sustain 7.5 fps when running YOLOv3. We further analyze the performance by showing that sharing the last-level cache with NVDLA can result in up to 1.56x speedup. We then identify that sharing the memory system with the accelerator can result in unpredictable execution time for the real-time tasks running on this platform. We believe this is an important issue that must be addressed in order for on-chip DNN accelerators to be incorporated in real-time embedded systems.

cs.DC↗

arXiv2018

Deterministic Memory Abstraction and Supporting Multicore System Architecture

Poor time predictability of multicore processors has been a long-standing challenge in the real-time systems community. In this paper, we make a case that a fundamental problem that prevents efficient and predictable real-time computing on multicore is the lack of a proper memory abstraction to express memory criticality, which cuts across various layers of the system: the application, OS, and hardware. We, therefore, propose a new holistic resource management approach driven by a new memory abstraction, which we call Deterministic Memory. The key characteristic of deterministic memory is that the platform - the OS and hardware - guarantees small and tightly bounded worst-case memory access timing. In contrast, we call the conventional memory abstraction as best-effort memory in which only highly pessimistic worst-case bounds can be achieved. We propose to utilize both abstractions to achieve high time predictability but without significantly sacrificing performance. We present deterministic memory-aware OS and architecture designs, including OS-level page allocator, hardware-level cache, and DRAM controller designs. We implement the proposed OS and architecture extensions on Linux and gem5 simulator. Our evaluation results, using a set of synthetic and real-world benchmarks, demonstrate the feasibility and effectiveness of our approach.

cs.AR↗