IEEE Boston Section
Upcoming Events!
Speaker: Steve Hoover, Redwood, EDA
Type of Course: Self-paced, on demand Course. Lab format
Dates: 9/1/2021 – 9/30/2021
Registration Deadline: Decision date to run/cancel this course: August 25, 2021
Register by Wednesday, August 25, 2021 for the early registration rate.
Members: $275.00 – Non-Members: $320
After August 25, 2021:
Members: $350.0 – Non Members: $395
Course Overview: CPUs are a fundamental building block of complex SoCs, and RISC-V is taking hold as the ISA of choice. In this workshop, you will create a Verilog RISC-V CPU from scratch, and you will modify this CPU to be suitable for different applications. You will learn and use modern techniques, using Transaction-Level Verilog to generate and modify your Verilog code more reliably, in far less time. You will discover how concepts like pipelining and hazards can be incorporated easily using timing-abstract design principles. All labs will be completed online in the Makerchip.com IDE for open-source circuit design. The skills you learn will be applicable far beyond CPU design. Outline of Topics to be Covered: Digital logic using TL-Verilog and Makerchip – combinational logic – sequential logic – pipelined logic – validity – a calculator circuit Basic RISC-V CPU microarchitecture – single-cycle CPU microarchitecture – testbench, test program, and lab setup for your CPU – fetch, decode, and execute logic for RISC-V subset – control flow logic Pipelined RISC-V subset CPU microarchitecture – simple pipelining of the CPU – hazards and PC redirects Completing the RISC-V CPU – data memory and load/store – remaining RISC-V (RV32I) instructions Course Format: – self paced, on demand course, providing attendees a flexible schedule – access to content for 30 days – pre-scheduled live Zoom and chat sessions with instructors during the 30 day access period – offline chat available with instructors during the entire 30 day access period (reply within 24 hours). Target Audience: Engineers interested in a career in digital logic design or adjacent disciplines, including experienced engineers looking to modernize their skill set. Prerequisites: An engineering education and basic understanding of digital logic. (Verilog knowledge is not a prerequisite.) Benefits of Attending: – Develop a solidified understanding of pipelined CPU design through hands-on labs. – Acquire knowledge of advanced digital circuit design methodology. – Gain exposure to an open-source design ecosystem. Speaker Bio: Steve Hoover is the founder of Redwood EDA, an early-stage startup focused on advanced silicon design methodology and tools. Steve is a former logic design lead for DEC, Compaq, and Intel and has extensive experience designing high-performance server CPUs and network switches. Social Media Profile: System Requirements: All resources are free and online; no download or installation required. We will use Slack, Zoom, GitHub Classroom, and Makerchip.com. Registration click here:
Geoscience & Remote Sensing Society
Speaker: Candence Brea Payne
Abstract:
Earth’s oceans are the largest defining feature of our planet and arguably an invaluable resource. Consequences of climate change threaten to have substantial and irreversible negative effects on our oceans, making it crucial to quickly understand and quantify behavioral changes resulting from increased human impact. Near-continuous, large-scale monitoring from space is revolutionizing methods for monitoring and forecasting ocean behavior. Nanosatellite platforms offer a potential solution for large-scale deployment of ocean-sensing instruments that provide detailed measurements of critical characteristics. Monitoring these key features provides valuable insight to behavioral changes within the context of our shifting climate.
Constellations of nanosatellites that target key ocean characteristics could provide continuous ocean monitoring with high spatiotemporal resolution. Compared with current state-of-the-art ocean-observing spacecraft, such as NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) with a repeat cycle of 16 days, nanosatellites in Low-Earth Orbit (LEO) can observe the same ground scene roughly once every five days. While spacecraft such as NASA’s Geostationary Operational. Environmental Satellite (GOES) achieves high temporal resolution, imaging the same scene every 30 seconds to 15 minutes depending on target region size, they are limited to imaging a single ground scene due to their stationary placement. Constellations of nanosatellites offer opportunities for measurement improvement including reducing revisit rates down from several days to hours, as well as increasing surface coverage through placement in orbital planes of varying inclinations. Informative, emergent information such as sea surface salinity, front location, and fauna concentrations (namely phytoplankton) are derived from measuring key characteristics such as ocean color and Sea Surface Temperature (SST). Existing nanosatellite constellations such as Planet’s Flock-3p, composed of 88 3U (10 x 10 x 30 cm) CubeSats, provide daily coverage of Earth’s land mass; however, they do not yet target oceans and coastal regions, nor tailor their imaging bands for these specific measurement needs. We present a concise set of ocean measurement band centers for an imaging payload targeting ocean color, a key behavioral feature. We assume narrow-band (10 – 15 nm bandwidth) ocean color measurements (390 nm – 865 nm) and constrain the payload to within the volume of a U-class (3U / 6U / 12U) nanosatellite located in LEO (~ 450 km altitude). A radiometric link approach is used to develop a tool that compares the performance of multiple different available Commercial
Off-the-Shelf (COTS) detectors, as well as different detector and optical front-end combinations. As detector sensitivity performance is driven primarily by aperture size and focal length, the imaging payload is assumed to have a scalable aperture (e.g.,diameter, focal length) and tunable sensor parameters (e.g., pixel pitch, number of pixels, sensor format). We simulate the sensor’s performance primarily by scaling the aperture from 0.5 cm to 20 cm diameter, suitable for 0.5U – 12U CubeSat volumes. Simulation results determine key “cut-off” regions where collected data no longer achieve the desired measured sensitivity of the target feature. A discussion of the radiometric approach, including definition of the measurement and detector parameter trade-space, is provided, along with preliminarily results of the simulated performance.
Bio:
Cadence Payne is a 4th year PhD student in the department of Aeronautics and Astronautics in the Space Telecommunications, Astronomy, and Radiation Laboratory advised by Dr. Kerri Cahoy. Her research at MIT focuses on technology development for small, Earth-observing spacecraft called CubeSats. She is currently the lead Systems Engineer for the Auroral Emission Radio Observer (AERO), a 3U CubeSat that uses a 4-meter vector sensor antenna to probe low-frequency emission from the Earth's aurora. She is also supporting AEROS, a joint mission with MIT Portugal that collects data for climate and weather monitoring via ocean observations. She graduated from Morehead State University in 2017 with a BS in Space Science and a minor in astronomy.
IEEE Computer Society and GBC/ACM
Ellie Pavlick
Register in advance for this webinar at
https://acm-org.zoom.us/webinar/register/3116263863253/WN_kBPd0z0MR7CDqJy26Gj0Mw
After registering, you will receive a confirmation email containing information about joining the webinar.
Many aspects of human language and reasoning are well explained in terms of symbols and rules. However, state-of-the-art computational models are based on large neural networks which lack explicit symbolic representations of the type frequently used in cognitive theories. One response has been the development of neuro-symbolic models which introduce explicit representations of symbols into neural network architectures or loss functions. In terms of Marr’s levels of analysis, such approaches achieve symbolic reasoning at the computational level (“what the system does and why”) by introducing symbols and rules at the implementation and algorithmic levels. In this talk, I will consider an alternative: can neural networks (without any explicit symbolic components) nonetheless implement symbolic reasoning at the computational level? I will describe several diagnostic tests of “symbolic” and “rule-governed” behavior and use these tests to analyze neural models of visual and language processing. Our results show that on many counts, neural models appear to encode symbol-like concepts (e.g., conceptual representations that are abstract, systematic, and modular), but not perfectly so. Analysis of the failure cases reveals that future work is needed on methodological tools for analyzing neural networks, as well as refinement of models of hybrid neuro-symbolic reasoning in humans, in order to determine whether neural networks’ deviations from the symbolic paradigm are a feature or a bug.
Ellie Pavlick is the Manning Assistant Professor of Computer Science at Brown University and a Research Scientist at Google AI.
Ellie received her PhD in Computer and Information Science from University of Pennsylvania in 2017. In 2012, she received a Bachelor of Arts in Economics from Johns Hopkins University and a Bachelor of Music in Saxophone Performance from the Peabody Conservatory. Ellie’s current research is in Natural Language Processing, specifically on computational models of semantics and pragmatics which emulate human inferences. She is interested in building better computational models of natural language semantics and pragmatics: how does language work, and how can we get computers to understand it the way humans do?
This joint meeting of the Boston Chapter of the IEEE Computer Society and GBC/ACM will be online only due to the COVID-19 lockdown.
Up-to-date information about this and other talks is available online at https://ewh.ieee.org/r1/boston/computer/.
You can sign up to receive updated status information about this talk and informational emails about future talks at https://mailman.mit.edu/mailman/listinfo/ieee-cs, our self-administered mailing list.
Speaker: Orhan Gazi, Cankaya University, Ankara-Turkey
Course Format: Live Webinar 10 sessions, one hour per session
Times and Dates: 10 – 11AM ET, September 14, 16, 21, 23, 28, 30, October 5, 7, 12, 14
Decision (Run/Cancel) Date for this course is: Wednesday, September 8, 2021
IEEE Member: $250.00
Non-Member: $300.00
Introduction: Forward error correction is a vital process in communication systems. The last channel codes discovered in the research world are the “polar codes” which are adapted to be used in 5G standard. The construction and decoding of polar codes are quite different from the construction and decoding of classical channel nodes. Polar codes are the only codes constructed in a non-trivial manner. The discovery of polar codes can be considered as a breakthrough in coding society. It is clear that future channel codes will follow the logic of polar codes. For this reason, it is critical to learn the encoding and decoding philosophy of the polar codes which is the state of art of the coding world.
Outline of the topics to be covered:
- Entropy and Mutual Information
- Philosophy of Polar Codes
- Generator Matrices of Polar Codes
- Polar Encoder Structures
- Recursive Structures for Polar Encoders
- Channel Splitting and Concept of Channel Polarization
- Split Channels
- Calculation of Split Channel Capacities
- Polar Decoding
- Polar Decoding for Noiseless Transmission
- Polar Decoding Formulas for Kernel Structure for noisy Transmission
- Successive Cancelation Decoding of Polar Codes
- Belief Propagation Decoding of Polar Codes
- Polar Encoders and Decoders in 5G New Radio (NR) and Future Channel Codes
Target Audience: Electronic and Communication Engineers, electronic engineers, computer engineers, engineers working in communication industry
Benefits of Attending Course:
1) The participant will have an idea about the state of art polar codes.
2) Polar codes are used in 5G standard; the participant can comprehend the polar code used in 5G standard.
3) The participant will learn successive cancelation decoding of polar codes.
Speaker Bio: Prof. Orhan Gazi is the author of the book “Polar Codes. A Non-Trivial Approach to Channel Coding” which can be reached from https://www.springer.com/gp/book/9789811307362
The book is selected by IEEE COMSOC as one of the best readings in polar codes, https://www.comsoc.org/publications/best-readings/polar-coding
Prof. Orhan Gazi is the sole author of 10 books written in electrical engineering subjects. Apart from the polar code book, he is the single author of the books “Information Theory for Electrical Engineers” https://www.springer.com/gp/book/9789811084317 and “Forward Error Correction via Channel Coding” https://www.springer.com/gp/book/9783030333799. The research area of Prof. Orhan Gazi involves “channel coding”, and “digital communication subjects”. Recently, he focuses on over capacity data transmission using polar codes. He is also interested in practical applications of communication systems involving FPGA devices. He is delivering courses with titles “VHDL circuit design”, “interface design using VHDL for FPGA devices” and “system on chip design”.
Materials to be included: Lecture slides will be provided.
Registration Fees:
IEEE Member: $250.00
Non-Member: $300.00
Decision (Run/Cancel) Date for this course is: Wednesday, September 8, 2021
Speaker: Orhan Gazi, Cankaya University, Ankara-Turkey
Course Format: Live Webinar 10 sessions, one hour per session
Times and Dates: 10 – 11AM ET, September 14, 16, 21, 23, 28, 30, October 5, 7, 12, 14
Decision (Run/Cancel) Date for this course is: Wednesday, September 8, 2021
IEEE Member: $250.00
Non-Member: $300.00
Introduction: Forward error correction is a vital process in communication systems. The last channel codes discovered in the research world are the “polar codes” which are adapted to be used in 5G standard. The construction and decoding of polar codes are quite different from the construction and decoding of classical channel nodes. Polar codes are the only codes constructed in a non-trivial manner. The discovery of polar codes can be considered as a breakthrough in coding society. It is clear that future channel codes will follow the logic of polar codes. For this reason, it is critical to learn the encoding and decoding philosophy of the polar codes which is the state of art of the coding world.
Outline of the topics to be covered:
- Entropy and Mutual Information
- Philosophy of Polar Codes
- Generator Matrices of Polar Codes
- Polar Encoder Structures
- Recursive Structures for Polar Encoders
- Channel Splitting and Concept of Channel Polarization
- Split Channels
- Calculation of Split Channel Capacities
- Polar Decoding
- Polar Decoding for Noiseless Transmission
- Polar Decoding Formulas for Kernel Structure for noisy Transmission
- Successive Cancelation Decoding of Polar Codes
- Belief Propagation Decoding of Polar Codes
- Polar Encoders and Decoders in 5G New Radio (NR) and Future Channel Codes
Target Audience: Electronic and Communication Engineers, electronic engineers, computer engineers, engineers working in communication industry
Benefits of Attending Course:
1) The participant will have an idea about the state of art polar codes.
2) Polar codes are used in 5G standard; the participant can comprehend the polar code used in 5G standard.
3) The participant will learn successive cancelation decoding of polar codes.
Speaker Bio: Prof. Orhan Gazi is the author of the book “Polar Codes. A Non-Trivial Approach to Channel Coding” which can be reached from https://www.springer.com/gp/book/9789811307362
The book is selected by IEEE COMSOC as one of the best readings in polar codes, https://www.comsoc.org/publications/best-readings/polar-coding
Prof. Orhan Gazi is the sole author of 10 books written in electrical engineering subjects. Apart from the polar code book, he is the single author of the books “Information Theory for Electrical Engineers” https://www.springer.com/gp/book/9789811084317 and “Forward Error Correction via Channel Coding” https://www.springer.com/gp/book/9783030333799. The research area of Prof. Orhan Gazi involves “channel coding”, and “digital communication subjects”. Recently, he focuses on over capacity data transmission using polar codes. He is also interested in practical applications of communication systems involving FPGA devices. He is delivering courses with titles “VHDL circuit design”, “interface design using VHDL for FPGA devices” and “system on chip design”.
Materials to be included: Lecture slides will be provided.
Registration Fees:
IEEE Member: $250.00
Non-Member: $300.00
Decision (Run/Cancel) Date for this course is: Wednesday, September 8, 2021
(Over 3 hours of instruction!)
Speaker: CL Kim
Reference book: “Neural Networks and Deep Learning” by Michael Nielsen, http://neuralnetworksanddeeplearning.com
Series Overview: From the book introduction: “Neural networks and deep learning currently provides the best solutions to many problems in image recognition, speech recognition, and natural language processing.”
This Part 1 and the planned Part 2 (winter or spring 2022, to be confirmed) series of courses will teach many of the core concepts behind neural networks and deep learning.
More from the book introduction: “We’ll learn the core principles behind neural networks and deep learning by attacking a concrete problem: the problem of teaching a computer to recognize handwritten digits. …it can be solved pretty well using a simple neural network, with just a few tens of lines of code, and no special libraries.”
“But you don’t need to be a professional programmer.”
The code provided is in Python, which even if you don’t program in Python, should be easy to understand with just a little effort.
Benefits of attending the series:
* Learn the core principles behind neural networks and deep learning.
* See a simple Python program that solves a concrete problem: teaching a computer to recognize a handwritten digit.
* Improve the result through incorporating more and more core ideas about neural networks and deep learning.
* Understand the theory, with worked-out proofs of fundamental equations of backpropagation for those interested.
* Run straightforward Python demo code example.
The demo Python program (updated from version provided in the book) can be downloaded from the speaker’s GitHub account. The demo program is run in a Docker container that runs on your Mac, Windows, or Linux personal computer; we plan to provide instructions on doing that in advance of the class.
(That would be one good reason to register early if you plan to attend, in order that you can receive the straightforward instructions and leave yourself with plenty of time to prepare the Git and Docker software that are widely used among software professionals.)
Course Background and Content: This is a live instructor-led introductory course on Neural Networks and Deep Learning. It is planned to be a two-part series of courses. The first course is complete by itself and covers a feedforward neural network (but not convolutional neural network in Part 1). It will be a pre-requisite for the planned Part 2 second course. The class material is mostly from the highly-regarded and free online book “Neural Networks and Deep Learning” by Michael Nielsen, plus additional material such as some proofs of fundamental equations not provided in the book.
Outline:
Introduction to Practical Neural Networks and Deep Learning (Part 1)
Feedforward Neural Networks.
* Simple (Python) Network to classify a handwritten digit
* Learning with Gradient Descent
* How the backpropagation algorithm works
* Improving the way neural networks learn:
** Cross-entropy cost function
** Softmax activation function and log-likelihood cost function
** Rectified Linear Unit
** Overfitting and Regularization:
*** L2 regularization
*** Dropout
*** Artificially expanding data set
*** Hyper-parameters
Pre-requisites: There is some heavier mathematics in learning the four fundamental equations behind backpropagation, so a basic familiarity with multivariable calculus and matrix algebra is expected, but nothing advanced is required. (The backpropagation equations can be also just accepted without bothering with the proofs since the provided Python code for the simple network just make use of the equations.) Basic familiarity with Python or similar computer language.
Speaker Background: CL Kim works in Software Engineering at CarGurus, Inc. He has graduate degrees in Business Administration and in Computer and Information Science from the University of Pennsylvania. He had previously taught for a few years the well-rated IEEE Boston Section class on introduction to the Android platform and API.
Decision to (Run/Cancel) Date for this course is Tuesday, September 14, 2021.
(Over 3 hours of instruction!)
HPEC is the largest computing conference in New England and is the premier conference in the world on the convergence of High Performance and Embedded Computing. We are passionate about performance. Our community is interested in computing hardware, software, systems and applications where performance matters. We welcome experts and people who are new to the field.
Speaker: Orhan Gazi, Cankaya University, Ankara-Turkey
Course Format: Live Webinar 10 sessions, one hour per session
Times and Dates: 10 – 11AM ET, September 14, 16, 21, 23, 28, 30, October 5, 7, 12, 14
Decision (Run/Cancel) Date for this course is: Wednesday, September 8, 2021
IEEE Member: $250.00
Non-Member: $300.00
Introduction: Forward error correction is a vital process in communication systems. The last channel codes discovered in the research world are the “polar codes” which are adapted to be used in 5G standard. The construction and decoding of polar codes are quite different from the construction and decoding of classical channel nodes. Polar codes are the only codes constructed in a non-trivial manner. The discovery of polar codes can be considered as a breakthrough in coding society. It is clear that future channel codes will follow the logic of polar codes. For this reason, it is critical to learn the encoding and decoding philosophy of the polar codes which is the state of art of the coding world.
Outline of the topics to be covered:
- Entropy and Mutual Information
- Philosophy of Polar Codes
- Generator Matrices of Polar Codes
- Polar Encoder Structures
- Recursive Structures for Polar Encoders
- Channel Splitting and Concept of Channel Polarization
- Split Channels
- Calculation of Split Channel Capacities
- Polar Decoding
- Polar Decoding for Noiseless Transmission
- Polar Decoding Formulas for Kernel Structure for noisy Transmission
- Successive Cancelation Decoding of Polar Codes
- Belief Propagation Decoding of Polar Codes
- Polar Encoders and Decoders in 5G New Radio (NR) and Future Channel Codes
Target Audience: Electronic and Communication Engineers, electronic engineers, computer engineers, engineers working in communication industry
Benefits of Attending Course:
1) The participant will have an idea about the state of art polar codes.
2) Polar codes are used in 5G standard; the participant can comprehend the polar code used in 5G standard.
3) The participant will learn successive cancelation decoding of polar codes.
Speaker Bio: Prof. Orhan Gazi is the author of the book “Polar Codes. A Non-Trivial Approach to Channel Coding” which can be reached from https://www.springer.com/gp/book/9789811307362
The book is selected by IEEE COMSOC as one of the best readings in polar codes, https://www.comsoc.org/publications/best-readings/polar-coding
Prof. Orhan Gazi is the sole author of 10 books written in electrical engineering subjects. Apart from the polar code book, he is the single author of the books “Information Theory for Electrical Engineers” https://www.springer.com/gp/book/9789811084317 and “Forward Error Correction via Channel Coding” https://www.springer.com/gp/book/9783030333799. The research area of Prof. Orhan Gazi involves “channel coding”, and “digital communication subjects”. Recently, he focuses on over capacity data transmission using polar codes. He is also interested in practical applications of communication systems involving FPGA devices. He is delivering courses with titles “VHDL circuit design”, “interface design using VHDL for FPGA devices” and “system on chip design”.
Materials to be included: Lecture slides will be provided.
Registration Fees:
IEEE Member: $250.00
Non-Member: $300.00
Decision (Run/Cancel) Date for this course is: Wednesday, September 8, 2021
SPECIAL NOTICE – CORONAVIRUS (COVID-19)
IEEE Boston Section recognized for Excellence in Membership Recruitment Performance
2021 Elected Officers
IEEE Boston Section was founded Feb 13, 1903, and serves more than 8,500 members of the IEEE. There are 29 chapters and affinity groups covering topics of interest from Aerospace & Electronic Systems, to Entrepreneur Network to Women in Engineering to Young Professionals. The chapters and affinity groups organize more than 100 meetings a year. In addition to the IEEE organization activities, the Boston Section organizes and sponsors up to seven conferences in any given year, as well as more than 45 short courses. The Boston Section publishes a bi-weekly newsletter and, currently, a monthly Digital Reflector newspaper included in IEEE membership.
The IEEE Boston Section also offers social programs such as the section annual meeting, Milestone events, and other non-technical professional activities to round out the local events. The Section also hosts one of the largest and longest running entrepreneurial support groups in IEEE.
More than 150 volunteers help create and coordinate events throughout the year.