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Job Description & How to Apply Below
DTMA & Memory Controller Design Lead
Location:
Bangalore, India |Company: LTSCT — L&T Semiconductor Technologies Limited
Reporting To:
Senior Architect, DC HW Systems |
Experience:
10+ years (mandatory)
Role Summary
The DTMA & Memory Controller Design Lead owns the Data Transfer and Memory Access (DTMA) engine together with HBM4e memory-controller integration. You will orchestrate all data movement between the 512-bank SRAM (8 TB/s), the 6-stack HBM4e subsystem (384 GB, 12 TB/s), and the PE arrays — the subsystem that keeps xPU's compute engine saturated.
Key Responsibilities
Own micro-architecture and RTL of the DTMA engine managing data movement between SRAM banks, HBM4e, and the BFOS PE arrays.
Define the compute-to-memory interface with the DTMA engine and SRAM banks to keep PE arrays fully fed d integration and configuration of HBM4e memory controllers across 6 stacks (384 GB, 12 TB/s aggregate).
Design descriptor-based DMA, gather/scatter, tensor-tiling, and prefetch mechanisms optimized for BFOS dataflow.
Architect the 512-bank, 8 TB/s SRAM access scheduling, bank arbitration, and conflict-avoidance logic.
Optimize address mapping, request scheduling, and refresh/timing to maximize sustained HBM bandwidth efficiency.
Define QoS and flow-control interfaces to the NoC and compute engine to prevent stalls and starvation.
Drive verification, performance modeling, and bandwidth/latency closure across realistic AI workload traffic.
Collaborate with PD on HBM PHY/controller placement and with architecture on memory-hierarchy trade-offs.
Required Skills & Experience
10+ years in memory-subsystem, DMA, or memory-controller design for high-bandwidth SoCs.
Deep expertise with DRAM/HBM controller architecture, scheduling, and timing (HBM2E/HBM3/HBM3E; HBM4e a strong plus).
Strong RTL (System Verilog) design skills for DMA engines, arbiters, and memory schedulers.
Solid understanding of multi-bank SRAM architectures, banking/interleaving, and conflict resolution.
Experience with descriptor-based DMA, gather/scatter, tensor tiling, and prefetching.
Knowledge of address mapping, request reordering, and bandwidth/efficiency optimization.
Familiarity with AI/ML data-movement patterns and their impact on memory-system design.
Hands-on performance modeling and closure for high-bandwidth memory subsystems.
Proficiency in C/C++/Python for modeling and verification.
Proven technical leadership and cross-functional collaboration.
Preferred Qualifications
Experience integrating HBM PHY/controller IP on advanced nodes.
Background in AI accelerator or HPC memory subsystems.
Familiarity with UCIe/NoC traffic interaction with memory controllers.
M.Tech/MS in EE/ECE or equivalent.
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