Jul 13, 2026
[paper] Scalable Open-Source Multi-Project SoC Architecture
May 2, 2024
[IC Design] Single Photon Counting ASIC for Synchrotron Applications
Abstract: The SPHIRD (Small Pixel High Rate photon counting Detector) project is an R&D study to investigate how far the photon counting X-ray hybrid pixel detector technology can go, regarding photon rate and spatial resolution. A goal was to boost by 30 times the count-rate capabilities of existing detectors of similar pixel size. SPHIRD targets that figure by designing fast front end electronics, by including pile-up compensation techniques in the pixel logic, and by implementing smaller pixels. Each pixel contains fast front-end analog electronics (pulse width is only 18ns) with base-line holder (BLH), a set of discriminators (with offset trimming blocks), ripple counters, and digital blocks. The pixel architecture allows also for operation in conventional mode (STDC) and with different pulse pile-up compensation methods (these are voltage and time based methods named VDIS, TDIS, and FPHC respectively).
Acknowledgements: The chip design was realized by P. Grybos, R. Kleczek, P. Otfinowski, and P. Kmon (AGH) while synchrotron experiments were conducted by P. Fajarado, D. Magalhaes and M. Raut.
References
[1] P. Grybos,et.al., “SPHIRD–Single Photon Counting Pixel Readout ASIC With Pulse Pile-Up Compensation Methods”, IEEE IEEE Transactions on Circuits and Systems--II: Express Briefs, vol. 70, no. 9, 2023, p. 3248-3252.
[2] D. Magalhaes et al., Very High Rate X-ray Photon Counting 2D Detectors with Small Pixels: the SPHIRD Project. 2022 IEEE NSS-MIC-RTSD Conference Proceedings.
Jan 5, 2024
ISHI-kai January 2024 event
ープンソースPDKやEDAの状況について、キーマンに語っていただきます
Schedule
Friday, January 26, 2024, 18:00-21:00 (Reception: 18:30)
Venue (onsite)
Google Shibuya Office
3-21-3 Shibuya, Shibuya-ku, Tokyo
Shibuya Stream Google reception meeting
Online Broadcast:
Google Meet: https://meet.google.com/ksa-tjaw-ges
Participation Fee
free
| Time | Speaker | Title | Lecture Outline |
|---|---|---|---|
| Until 18:30 | ISHI-kai | reception | The entrance to the facility closes at this time, so if you are participating locally, please come by this time as much as possible. |
| 18:00 ~ 18:30 | ISHI-kai | Chat time | - |
| 18:30 ~ 19:15 (Lecture: 30min, Q&A: 15min) | Takeshi Hamamoto Minimal Fab Propulsion Organization Device Engineer | minimal Fab open PDK | 1) What is a minimal fab 2) openPDK 3) Design Contest at Semicon 2023 |
| 19:15 ~ 20:00 (Lecture: 30min., Q&A: 15min.) | Junichi Okamura IEEE Senior Member | OpenPDK and the World | - |
| 20:00 ~ 20:45 (Lecture: 30min., Q&A: 15min.) | @noritsuna | About the upcoming open source PDK shuttle | (To be released at a later date) |
| 21:00 | ISHI-kai | closing |
Apr 6, 2023
[Deadline] #TinyTapeout 3
#TinyTapeout and #SiliWiz are online tools you can use to learn how ASICs are designed, made and how they work. You can even get your designs affordably manufactured!
Matt Venn has some free slots for #TinyTapeout 3 for you and your students - just send him a DM to get started!
Deadline is 24th April! Apply today at https://tinytapeout.com/
Feb 8, 2023
[paper] OpenSpike: An OpenRAM SNN Accelerator
OpenSpike: An OpenRAM SNN Accelerator
arXiv:2302.01015v1 [cs.AR] 2 Feb 2023
1) Dept. of Electrical Engineering Allameh Mohaddes Nouri University Nur, Mazandaran, Iran
2) Dept. of Computer Science and Engineering, UC Santa Cruz Santa Cruz, CA, United States
3) Dept. of Electrical and Computer Engineering, UC Santa Cruz Santa Cruz, CA, United States
Abstract: This paper presents a spiking neural network (SNN) accelerator made using fully open-source EDA tools, process design kit (PDK), and memory macros synthesized using Open- RAM. The chip is taped out in the 130 nm SkyWater process and integrates over 1 million synaptic weights, and offers a reprogrammable architecture. It operates at a clock speed of 40 MHz, a supply of 1.8 V, uses a PicoRV32 core for control, and occupies an area of 33.3 mm2. The throughput of the accelerator is 48,262 images per second with a wallclock time of 20.72 μs, at 56.8 GOPS/W. The spiking neurons use hysteresis to provide an adaptive threshold (i.e., a Schmitt trigger) which can reduce state instability. This results in high performing SNNs across a range of benchmarks that remain competitive with state-of-the-art, full precision SNNs.
The design is open sourced and available online: https://github.com/sfmth/OpenSpike



