Our new paper, which describes our recent work on creating a framework that allows co-simulation of server systems with PCIe-connected FPGAs, has been accepted to appear at FPGA 2018. We are also planning an open source release of this framework.
“A Full-System VM-HDL Co-Simulation Framework for Servers with PCIe-Connected FPGAs.” Shenghsun Cho, Mrunal Patel, Han Chen, Peter Milder, and Michael Ferdman. To appear at FPGA 2018.
Abstract: The need for high-performance and low-power acceleration technologies in servers is driving the adoption of PCIe-connected FPGAs in datacenter environments. However, the co-development of the application software, driver, and hardware HDL for server FPGA platforms remains one of the fundamental challenges standing in the way of wide-scale adoption. The FPGA accelerator development process is plagued by a lack of comprehensive full-system simulation tools, unacceptably slow debug iteration times, and limited visibility into the software and hardware at the time of failure.
In this work, we develop a framework that pairs a virtual machine and an HDL simulator to enable full-system co-simulation of a server system with a PCIe-connected FPGA. Our framework enables rapid development and debugging of unmodi ed application software, operating system, device drivers, and hardware design.
Once the system is debugged, neither the software nor the hardware requires any changes before being deployed in a production environment. In our case studies, we nd that the co-simulation framework greatly improves debug iteration time while providing invaluable visibility into both the software and hardware components.
Please click here for a pre-print [pdf].
The National Science Foundation has funded our work that aims to create a flexible hardware and software framework for next generation edge computing devices.
Please read more in this article at the Stony Brook College of Engineering and Applied Sciences website.
Our new paper on improving the efficiency of hardware accelerators for convolutional neural networks has been accepted for publication at the 44th International Symposium on Computer Architecture (ISCA), 2017.
This paper, co-authored with Yongming Shen (Stony Brook CS PhD student) and Stony Brook CS professor Mike Ferdman, proposes a new Convolutional Neural Network (CNN) accelerator paradigm and an accompanying automated design methodology that partitions the available FPGA resources into multiple processors, each of which is tailored for a different subset of the CNN convolutional layers.
Yongming Shen, Michael Ferdman, and Peter Milder. “Maximizing CNN Accelerator Efficiency Through Resource Partitioning.” To appear at The 44th International Symposium on Computer Architecture (ISCA), 2017.
You can read a pre-print here.
Our new paper on bandwidth-efficient hardware accelerators for convolutional neural networks will appear at FCCM 2017. This paper, co-authored with Stony Brook CS PhD student Yongming Shen and Stony Brook CS professor Mike Ferdman, proposes a new method to efficiently balance between the transfer costs of CNN data and CNN parameters and describes a new flexible architecture that is able to reduce the overall communication requirement.
Abstract—Convolutional neural networks (CNNs) are used to solve many challenging machine learning problems. Interest in CNNs has led to the design of CNN accelerators to improve CNN evaluation throughput and efficiency. Importantly, the bandwidth demand from weight data transfer for modern large CNNs causes CNN accelerators to be severely bandwidth bottlenecked, prompting the need for processing images in batches to increase weight reuse. However, existing CNN accelerator designs limit the choice of batch sizes and lack support for batch processing of convolutional layers.
We observe that, for a given storage budget, choosing the best batch size requires balancing the input and weight transfer. We propose Escher, a CNN accelerator with a flexible data buffering scheme that ensures a balance between the input and weight transfer bandwidth, significantly reducing overall bandwidth requirements. For example, compared to the state-of-the-art CNN accelerator designs targeting a Virtex-7 690T FPGA, Escher reduces the accelerator peak bandwidth requirements by 2.4× across both fully-connected and convolutional layers on fixed-point AlexNet, and reduces convolutional layer bandwidth by up to 10.5× on fixed-point GoogleNet.
Yongming Shen, Michael Ferdman, and Peter Milder. “Escher: A CNN Accelerator with Flexible Buffering to Minimize Off-Chip Transfer.” To appear at The 25th IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM), 2017.
You can read a preprint here.
A new article focusing on hardware implementation of execution stream compression will appear in ACM Transactions on Embedded Computing Systems, in a special issue on Secure and Fault-tolerant Embedded Computing. This paper was co-authored with Maria Isabel Mera (a Stony Brook ECE MS alum, currently a PhD student at NYU), Jonah Caplan and Seyyed Hasan Mozafari (graduate students at McGill University), and Prof. Brett Meyer from McGill. This work was based in part on Maria Isabel Mera’s MS thesis.
“Area, Throughput and Power Trade-offs for FPGA- and ASIC-based Execution Stream Compression.” Maria Isabel Mera, Jonah Caplan, Seyyed Hasan Mozafari, Brett H. Meyer, and Peter Milder. To appear in ACM Trans. on Embedded Computing Systems, 2017.
Abstract: An emerging trend in safety-critical computer system design is the use of compression, e.g., using cyclic redundancy check (CRC) or Fletcher Checksum (FC), to reduce the state that must be compared to verify correct redundant execution. We examine the costs and performance of CRC and FC as compression algorithms when implemented in hardware for embedded safety-critical systems. To do so, we have developed parameterizable hardware generation tools targeting CRC and two novel FC implementations. We evaluate the resulting designs implemented for FPGA and ASIC and analyze their efficiency; while CRC is often best, FC dominates when high throughput is needed.
Please check back later for a pre-print.
A new paper, entitled “Practical Matlab Experience in Lecture-Based Signals and Systems Courses,” which I co-authored with Prof. Mónica Bugallo, will appear at the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), in the special session on Advances in Signal Processing Education.
Yongming Shen will be presenting a poster on our current work to implement bandwidth-efficienct fully-connect neural network layers next month.
Yongming Shen, Michael Ferdman, and Peter Milder. “Storage-Efficient Batching for Minimizing Bandwidth of Fully-Connected Neural Network Layers.” Poster to appear at FPGA 2017.
The National Science Foundation’s Enhancing Access to the Radio Spectrum program has funded our group’s work on efficient distributed spectrum sensing. The goal of this work is to enable crowd-sourced collaborative spectrum sensing including low-cost low-power FPGA-based hardware and novel interpolation and optimization techniques to aggregate and analyze data.
This work is a collaboration with Samir Das and Himanshu Gupta (Stony Brook CS), and Petar Djurić (Stony Brook ECE).
You can read more at the NSF website.
I am organizing the 2016 MEMOCODE Design Contest, which begins today and lasts through September 13.
This year’s contest problem is will be k-means clustering. You can read the contest description here, and read more about MEMOCODE 2016 here.
Our new paper “Fused Layer CNN Accelerators” by Manoj Alwani, Han Chen, Michael Ferdman, and Peter Milder has been accepted to appear at MICRO 2016.
A preprint is available here.
In this work, we observe that a previously unexplored dimension exists in the design space of CNN accelerators that focuses on the dataflow across convolutional layers. We find that we are able to fuse the processing of multiple CNN layers by modifying the order in which the input data are brought on chip, enabling caching of intermediate data between the evaluation of adjacent CNN layers. We demonstrate the effectiveness of our approach by constructing a fused-layer CNN accelerator for the first five convolutional layers of the VGGNet-E network, and find that, by using 362KB of on-chip storage, our fused-layer accelerator minimizes off-chip feature map data transfer, reducing the total transfer by 95%, from 77MB down to 3.6MB per image.