Customer Support Processor
Resume:
References available upon request
Al Wegener
**.*******@*****.***
*** ***** **** 408-***-**** (mobile)
Scotts Valley, CA 95066
Experienced CTO and highly technical leader/manager/inventor in both large companies and start-ups. Founded Anacode in 2015: faster, more affordable that uses microservices like AWS Lambda and containers/Kubernetes to implement massively parallel, scalable, lossless compression. Founded Samplify, Inc. (2006-2013): raised
$23M+ in capital from VCs (Series A) and strategics (Schlumberger; oil & gas; led Series B). Deep intellectual property (patents & trade secrets), algorithm development,
& product definition experience. Management and technical leadership of engineering groups as large as 25 people. Significant business development successes (international and US) in data centers, medical imaging, wireless, and pro & consumer audio. Prolific writer (articles, conferences, patents) and seasoned public speaker. Al also loves to educate others. Al’s natural curiosity may be one of his most important characteristics. Education: MS, Computer Science Stanford University BS, Electrical Engineering Bucknell University
LinkedIn Profile: https://www.linkedin.com/in/alwegener Professional Experience:
4/15 – present Anacode Labs, Aptos (Santa Cruz), CA Founder and CEO
Founded Anacode Labs, Inc. to develop massively parallel lossless compression and decompression that creates faster, more affordable storage. Researched and developed the Anacode encoding and decoding algorithm in ANSI C. Prototyped certain algorithmic steps in Matlab. Architect of the Anacode hardware micro-architecture in FPGAs and SoCs, targeting a Xilinx FPGA (VU9P) in AWS F1 Instances. Visited more than 100 prospects (2015-present) to better understand the features that Cloud SW, storage, and PaaS vendors wanted in a Cloud storage service. Deep familiarity of public (AWS, Azure, GCP) and private Cloud (data center) ecosystems (WWT, CDW; Cisco, Dell EMC, HPE, NetApp, Pure, etc.). Developed Anacode pricing models and pro forma revenue and expense projections.
12/13 - 3/15 Amazon Lab126, Sunnyvale, CA
Principal Engineer
Hired as 1 of 3 principal engineers for a face recognition IC, targeting 20 nm CMOS. Had the SoC taped out, it would have performed the Viola-Jones face recognition algorithm at 35+ frames/sec using 100 mW, replacing a software implementation of the Viola-Jones algorithm that only achieved 14 frames/sec using 1 Watt and one Qualcomm CPU. After the Fire Phone was cancelled in August 2014, I joined a machine learning group that was prototyping future Lab126 products. In late 2014 Amazon’s Elastic Block Store group in Seattle (which purchases many Solid-State Disks [SSDs]) asked me to perform a Wegener, page 2 of 4
References available upon request
“compression analytics” study using customer data from AWS S3, to explore certain compression alternatives. After completing this study in Feb 2015, I resigned from Amazon to work on Anacode full time.
7/06 - 12/13 Samplify Systems, Santa Clara, CA
CTO, Chairman, and Founder
Founded Samplify Systems, a venture-backed high-speed lossy numerical compression start-up. Named on nearly all Samplify’s 55+ granted patents. Developed the real-time compression technology, wrote compress/decompress software in C, managed the development of the FPGA hardware prototype, raised a $300k seed round from Charles River Ventures, and attracted a world-class engineering, sales, and marketing team to bring the Samplify vision to market. Raised $12M Series A from Charles River Ventures and Formative Ventures; Raised $11M Series B from Schlumberger (oil & gas). Visited 50+ customers in US, Europe, and Asia to describe the benefits of real- time compression for medical imaging (CT, ultrasound, MRI), wireless (OBSAI/CPRI, remote radio heads, WiMax), and data converter (A/D, D/A) applications. Active in recruiting and hiring a talented staff of 30+ engineers and marketing professionals. 8/02 - 7/06 Texas Instruments, Palo Alto, CA
Manager, Wireless Infrastructure Radio Products
Manage a team of 16 engineers that are developing high-speed ASICs for the wireless infrastructure (UMTS, cdma2000, WiMax, etc.). ~$10M in annual sales. The Palo Alto team includes engineers from the start-up formerly known as Graychip, a leader in the digital upconverter (DUC) and digital downconverter (DDC) market. TI Palo Alto also develops crest factor reduction (CFR) and digital predistortion (DPD) ASICs that improve the efficiency of multi-carrier power amplifiers (MCPAs). Responsible for product definition, ASIC and SW design management, customer support, & liaison with TI Dallas headquarters. Wireless DSP Architect (Aug 02 – May 04)
Systems engineer and project manager for the GC1115, an innovative crest factor reduction
(CFR) processor that improves the efficiency of cellular power amplifiers by 2x. The GC1115 is a 1.8M gate, 0.13 micron, 305 MHz clock, 256-PBGA device for UMTS and cdma2k base stations. Wrote the GC1115 specification, all Matlab code for bit-accurate device simulation, generated ~600 test vectors for RTL (Verilog) testing, briefed customers on the GC1115, wrote the data sheet. Managed the GC1115 release to market (RTM). Jan 00 – Jun 00 Morphics Technology, Inc., Campbell, CA; Director of Applications Aug 96 – Jan 00 Command Audio Corp., Redwood City, CA; Principal Engineer Jun 96 – Jul 96 Spectrum Wireless, Inc., Mountain View, CA; Director of DSP May 90 – May 95 Studer Editech Corporation, Menlo Park, CA; VP Engineering (93-95), Sr. DSP Eng. (90-93) Jul 85 – Apr 90 GTE Government Systems, Mountain View, CA, DSP Section Manager, staff of ~10 Mar 84 – Jun 85 Atari Corporate Research, Sunnyvale, CA, Member of the Research Staff (audio DSP) Jun 81 – Feb 84 GTE Government Systems, Mountain View, CA, DSP Engineer Wegener, page 3 of 4
References available upon request
Patents: Total of 60+ US patents, including the following: 1. US 11,245,416 ANACODE: https://patents.google.com/patent/US11245416B2/en?oq=11245416 2. US 11.0640,55 ANACODE: Accelerated data center transfers (turn 100 Gbps into 400 Gbps Ethernet) 3. US 10,972,125 ANACODE: Storage access interface to an encoded storage system a. API calls GET & PUT for lossless–compressed random access 4. US 9,942,222 AMZN: Authentication with wearable device 5. US 9,641,526 AMZN: Location based authentication methods and systems 6. US 9,398,007 AMZN: Deferred authentication methods and systems 7. US 9,319,063 SAMP: Enhanced multi-processor waveform data exchange using comp and decomp 8. US 9,298,457 SAMP: SIMD instructions for data compression and decompression 9. US 9,274,802 SAMP: Data compression and decompression using SIMD instructions 10. US 9,240,803 SAMP: Compression of baseband signals in base transceiver system processors 11. US 9,158,686 SAMP: Processing system and method including data compression API 12. US 9,118,345 SAMP: Data compression profiler for configuration of compression 13. US 9,106,936 SAMP: Raw format image data processing 14. US 9,104,473 SAMP: Conversion and compression of floating-point and integer data 15. US 9,055,472 SAMP: Transmission of multiprotocol data in a distributed antenna system 16. US 9,047,118 SAMP: Computationally efficient compression of floating-point data 17. US 9,026,568 SAMP: Data compression for direct memory access transfers 18. US 8,959,130 SAMP: Compression of floating-point data in encoding groups 19. US 8,959,129 SAMP: Compression of floating-point data 20. US 8,880,734 SAMP: Block floating point comp with exponent token codes 21. US 8,874,794 SAMP: Block floating point comp with exponent difference and mantissa coding 22. US 8,795,180 SAMP: Ultrasound signal compression 23. US 8,705,634 SAMP: Compression of baseband signals in base transceiver systems 24. US 8,649,388 SAMP: Transmission of multiprotocol data in a distributed antenna system 25. US 8,631,055 SAMP: Enhanced multi-processor waveform data exchange using comp and decomp 26. US 8,331,461 SAMP: Compression of baseband signals in base transceiver system radio units 27. US 8,320,433 SAMP: Compression of baseband signals in base transceiver system interfaces 28. US 8,317,706 SAMP: Post-beamforming compression in ultrasound systems 29. US 8,301,803 SAMP: Block floating point compression of signal data 30. US 8,174,428 SAMP: Compression of signals in base transceiver systems 31. US 8,157,738 SAMP: Ultrasound signal compression 32. US 8,151,022 SAMP: Comp and storage of projection data in a rotatable part of a CT system 33. US 8,045,811 SAMP: Comp and storage of projection data in a computed tomography system 34. US 8,005,152 SAMP: Compression of baseband signals in base transceiver systems 35. US 7,916,830 SAMP: Edge detection for computed tomography projection data compression 36. US 7,893,852 SAMP: Enhanced time-interleaved A/D conversion using compression 37. US 7,876,252 SAMP: Compression of sampled data in a data acquisition system US 38. US 7,852,977 SAMP: Adaptive compression of computed tomography projection data 39. US 7,796,078 SAMP: Enhanced time-interleaved A/D conversion using compression 40. US 7,650,249 SAMP: Data compression for a waveform data analyzer 41. US 7,541,950 SAMP: Enhanced time-interleaved A/D conversion using compression 42. US 7,522,074 SAMP: Enhanced control for compression and decompression of sampled signals 43. US 7,088,276: SAMP: Enhanced data converters using compression and decompression 44. US 7,071,852: SAMP: Enhanced test and measurement inst using comp and decomp 45. US 7,009,533: SAMP: Adaptive compression and decompression of bandlimited signals 46. US 5,839,100: SAMP: Lossless and loss-limited sampled data compression Wegener, page 4 of 4
References available upon request
Publications:
1. A methodology for evaluating the impact of data compression on climate simulation data (ACM, 2014) 2. Assessing the effects of data compression in simulations using physically motivated metrics (SC’13 Best Paper Finalist; Denver, CO)
3. Evaluating Lossy Compression on Climate Data (ISC’13; Hamburg, DE) 4. Real-time compression of raw computed tomography data: technology, architecture, and benefits, SPIE Symposium on Medical Imaging, Feb 2009. Co-author: Bob Senzig, chief engineer, GE Healthcare. 5. Samplify FPGA Design Specifications for Prism 2.0 Compression (100+ pages), Sep-Dec 2008. 6. Data Compression for High-Speed DSP Systems, on-line article, July 2008, www.dspdesignline.com. 7. (moderator) Quality vs. Time to Market: The Unmentionable Tradeoff, Design Automation Conference, Anaheim, CA, June 2008.
8. Compressing the Storage Demands for CT Imaging, Medical Design Technology, Vol. 11, No. 9 (Sep 2007), www.mdtmag.com
9. High-speed, Real-time Compression for Instrumentation and Measurement, IEEE Boston Instruments and Measurement Society, Sep07.
10. Nyquist Revisited: How Many Samples are Really Necessary? Planet Analog web site. 11. http://www.en-genius.net/includes/files/dspt_070207.pdf, June 2007. 12. Equivalent Results: A methodology to measure the effects of high-speed compression, 13. www.networksystemsdesignline.com web site, May 2007. 14. High-Performance Crest Factor Reduction Processor for W-CDMA and OFDM Applications, IEEE RF Integrated Circuits Conference, June 2006.
15. Samplify: High-speed Compression for Test & Measurement Applications, Evaluation Eng, Dec 2005. 16. Samplify: Lossless and Lossy Sampled Data Compression for SDR, SDR Forum, Nov 2005. 17. CFR: Subtract Peaks to Add Value, Wireless Design & Development, Oct 2005. 18. Configuring the GC1115 for 3G Air Interfaces, Texas Instruments Application Note SLWA045, May 2005, http://focus.ti.com/lit/an/slwa045/slwa045.pdf 19. GC1115 Crest Factor Reduction Processor, Texas Instruments Data Sheet SLWS144C, March 2005, http://focus.ti.com/lit/ds/symlink/gc1115.pdf
20. Crest Factor Reduction and DPD in Wireless Systems, TI Developer Conference (Houston, TX), Feb.05 21. Samplify: Compression of Bandlimited A/D and D/A Converter Samples at 100 Msamp/sec, GSPx Conference (Santa Clara, CA), Sep 2004.
22. MUSICompress: Lossless, Low-MIPS Audio Compression in Software and Hardware, ICSPAT, Sep97. 23. Parallelizing High Quality Audio Compression Algorithms, Audio Engineering Society Conference, Mar93 (Berlin, Germany), Preprint 3492.
24. Practical Techniques for Fast Baud Rate Estimation, IEEE ICASSP-92, Mar92. Languages: Matlab (since 1990), C (MSVC and gcc), assembly (TI TMS320C5x, ‘C67x, Moto 56xxx, MS Visual Studio Express, TI Code Composer Studio, R, Python, Bash scripts. Interests: High-speed lossless and lossy sampled data compression Cloud technology (data centers); Genomics & Analytics Medical imaging (ultrasound, CT, MRI); Digital audio broadcasting FPGA and ASIC implementations of DSP algorithms
Wireless communications (3GPP, LTE, WiMAX, TD-SCDMA; WiFi) DSP- and FPGA-related start-ups; DSP market (HW, SW, and systems vendors) High-quality lossy coding (for speech, audio, and video; H.264/H.265 Personal: Musician (piano, guitar, vocals) Married, 6 children (all “launched”) United Mileage Plus 1k member Fluent in German
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