Successful chip designers understand that maximizing MAC count in AI/ML accelerator blocks at the cost of cutting back on memory is not a viable strategy. While silicon cost is a concern, compromising on memory resources can impede performance and hinder overall success. In the complex electronics supply chain, where multiple entities collaborate, it becomes challenging to predict future ML workloads and system behaviors accurately. So, how can chip designers make informed choices without defaulting to “Max TOPS / Min Area”?
Assumptions can be fatal in this process. Many SoC teams rely on in-house accelerators for machine learning inference, which often lack accurate simulation models and require time-consuming gate-level simulations. This limited information often leads to deadly assumptions. One common mistake is assuming that memory usage patterns will remain unchanged as networks evolve. Another risky assumption is assigning a fixed percentage of external bandwidth without considering resource contention over time.
Adding more SRAM as buffer memory may seem like an obvious solution, but it may not address the problem entirely. Hardwired state machine accelerators with inflexible memory access patterns can still generate excessive tiny block transfer requests, adversely affecting performance. The key lies in finding the right balance of memory.
The solution lies in two aspects. First, selecting a machine learning inference processing solution that intelligently manages local SRAM memory with flexible, code-driven implementations of new networks can minimize external requests. Second, choosing an acceleration solution that smartly prefetches data anticipated to be needed ahead in the graph execution allows the subsystem to tolerate variable response times from on-chip and off-chip memory resources.
Quadric’s Chimera GPNPU addresses the memory challenge with its intelligent approach. By analyzing data usage across ML graphs and leveraging advanced operator fusion techniques, Quadric’s technology eases memory bottlenecks. The Chimera GPNPU offers a range of local buffer memory configurations (1 MB to 32 MB) to suit different system requirements. Contrary to the assumption that larger local memories are necessary for good performance, Quadric’s solution demonstrates remarkable tolerance to system resource contention even with relatively small local memory configurations.
Extensive system simulation capabilities and smart data prefetching provided by Quadric’s Chimera Graph Compiler further enhance the resilience of the system, ensuring optimal performance. With Quadric’s ML solution, chip designers can make confident resource choices and avoid the agony of uncertainty. By choosing a solution that offers programmability, modeling capability, and intelligent memory management, designers can be certain of their choices before tapeout, leading to successful chip designs with superior AI/ML acceleration capabilities.
Frequently Asked Questions:
1. Why is compromising on memory resources not a viable strategy for chip designers?
Compromising on memory resources can impede performance and hinder overall success in AI/ML accelerator blocks. While silicon cost is a concern, maximizing MAC (Multiply-Accumulate) count at the expense of cutting back on memory can limit the performance of the chip.
2. What challenges do chip designers face in predicting ML workloads and system behaviors?
In the complex electronics supply chain, where multiple entities collaborate, it becomes challenging to accurately predict future ML workloads and system behaviors. This lack of accurate information can lead to assumptions that may prove fatal in the design process.
3. Why is it risky to assume that memory usage patterns will remain unchanged as networks evolve?
Assuming that memory usage patterns will remain unchanged as networks evolve is risky because new networks may have different memory access patterns. This can result in inadequate memory resources and adversely affect performance.
4. What is the key to finding the right balance of memory?
The key to finding the right balance of memory lies in two aspects. Firstly, selecting a machine learning inference processing solution that intelligently manages local SRAM memory with flexible, code-driven implementations of new networks can minimize external requests. Secondly, choosing an acceleration solution that smartly prefetches data anticipated to be needed ahead in the graph execution allows the subsystem to tolerate variable response times from on-chip and off-chip memory resources.
5. How does Quadric’s Chimera GPNPU address the memory challenge?
Quadric’s Chimera GPNPU addresses the memory challenge by analyzing data usage across ML graphs and leveraging advanced operator fusion techniques. It offers a range of local buffer memory configurations (1 MB to 32 MB) to suit different system requirements. The solution demonstrates remarkable tolerance to system resource contention even with relatively small local memory configurations.
6. How does Quadric’s ML solution enhance the resilience of the system?
Along with the Chimera GPNPU, Quadric’s ML solution provides extensive system simulation capabilities and smart data prefetching through the Chimera Graph Compiler. These features enhance the resilience of the system, ensuring optimal performance by prefetching data and making informed choices.
7. How can chip designers benefit from Quadric’s ML solution?
Chip designers can benefit from Quadric’s ML solution by making confident resource choices and avoiding uncertainty. The solution offers programmability, modeling capability, and intelligent memory management, allowing designers to be certain of their choices before the tapeout process. This can lead to successful chip designs with superior AI/ML acceleration capabilities.
Definitions:
– SoC: System-on-Chip
– SRAM: Static Random-Access Memory
– ML: Machine Learning
– GPNPU: General-Purpose Neural Processing Unit
– MAC: Multiply-Accumulate
Suggested Related Links:
– Quadric (Main website of Quadric, the company mentioned in the article)
The source of the article is from the blog lisboatv.pt