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Why Latency-Tolerant Architectures Matter in the Age of AI Supercomputing
High Bandwidth Memory (HBM) has become the defining enabler of modern AI accelerators. From NVIDIA’s GB200 Ultra to AMD’s MI400, every new AI chip boasts faster and larger stacks of HBM, pushing memory bandwidth into the terabytes-per-second range. … Read More
Because of its open and modular nature, RISC-V has faced recognizable security challenges stemming from fragmentation, performance inefficiencies, and inherent vulnerabilities. Fragmentation across implementations leads to inconsistencies, making it difficult to enforce uniform security measures. Performance… Read More
The dominance of GPUs in AI workloads has long been driven by their ability to handle massive parallelism, but this advantage comes at the cost of high-power consumption and architectural rigidity. A new approach, leveraging a chiplet-based RISC-V vector processor, offers an alternative that balances performance, efficiency,… Read More
As RISC-V gains traction in the global semiconductor industry, developers are exploring fully open-source approaches to processor design. XiangShan, a high-performance RISC-V CPU project, combined with the Mulan Permissive License v2 (Mulan PSL v2), represents a community-driven, transparent alternative to proprietary… Read More
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Disaggregating LLM Inference: Inside the SambaNova Intel Heterogeneous Compute Blueprint