The semiconductor industry is up against a wall. For years, computer processors have spent the vast majority of time fetching data from external memory banks. That limits how quickly the chips can finish tasks, capping performance.

To get around that memory wall, a semiconductor start-up is looking to process information inside memory, which could vastly improve bandwidth and latency. The Grenoble, France-based Upmem recently raised $3.6 million from investors to make the processing-in-memory chips, which have sat on the sidelines of the chip industry for the past two decades.

“When Moore’s Law was strong, and provided twice the processing power every 18 months, it was useless to try something else,” said Jean-Francois Roy, Upmem’s chief operating officer, in an email. “Now, we see that all the big players in the data center are looking at heterogeneous computing because they need a complementary solution for big data.”

Founded in 2015 and partially funded by Western Digital, the company is starting to get its chips ready for manufacturing. The chips could tackle machine learning tasks more efficiently by skirting the memory wall and improve algorithms that analyze financial data to trade stocks and human genes to personalize drugs.

In its chips, Upmem combines main memory with thousands of DRAM processing units, which sit directly next to data. The specialized silicon is linked to a traditional processor, which runs an operating system and offloads tasks to the battalion of DPUs. The result is 20 times better performance without using any additional power, Roy said.

Upmem is building a coprocessor for server chips from Intel and Advanced Micro Devices, not an accelerator for machine learning tasks in the style of Google and Nvidia. The company plans to package 16 chips into DIMM modules, which can be inserted into standard motherboard slots. That lets companies avoid costly changes to server hardware, Roy said.

The idea is not exactly new. David Patterson – a former professor at the University of California at Berkeley, one of the pioneers in reduced instruction set computing, and an architect of Google’s tensor processing unit – proposed what he called intelligent random-access memory two decades ago. The goal was to cut down on the speed gap between processing and memory before it got too wide.

Micron, one of the world’s biggest memory chipmakers, has built a processing-in-memory chip called Automata to find patterns in unstructured financial and biomedical data. Venray, founded by chip architect Russel Fish, is aiming to license similar server chips so that companies can stop adding bigger and bigger caches, where instructions and data are stored for quick access.

Upmem’s executives said that its chips had several advantages over previous attempts at processing in DRAM, which is naturally parallel like GPUs and FPGAs. Instead of squeezing more memory into computer processors, Upmem is “adding processing units to existing DRAM so that we don’t reinvent the wheel,” Roy said.

The company plans to provide tools so that central processors can be programmed to hand out workloads to thousands of DPU cores located in main memory – something that should make it easier to experiment with its chips. Roy also said that the its tools simplify programming, compiling, and debugging software written for the DPUs.

Upmem has spent two years preparing its chips but would not say when the technology would be released. The company said that it needs memory chipmakers like SK Hynix, Samsung, Micron, and Western Digital to manufacture the DPU, but it declined to comment on specific partnerships.

By: DocMemory
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