DeepSim, Inc.

What do they actually do

DeepSim is building an AI‑accelerated 3D physics simulator focused on thermal analysis for semiconductor and AI chip design. The product is in active development and currently being validated with semiconductor companies, including an engagement with Intel, indicating a pilot/validation stage rather than broad commercial availability (YC profile).

Their approach combines lightweight AI models with a GPU‑accelerated solver to automate manual setup (like meshing), run much larger multi‑scale simulations, and return results faster than traditional FEM tools. They claim capabilities such as billion‑node thermal simulations on a single GPU, ~10‑minute demo runs, and “1000× faster” as a design goal—these are company‑stated metrics pending independent benchmarks (DeepSim site, YC profile, press).

A typical workflow: engineers bring a chip/package design and a thermal question; DeepSim automates setup with AI and runs a GPU solver; the platform returns thermal maps and hotspot resolution across scales (from transistor‑level hot spots to full chip/package) to enable faster iteration and, in pilots, digital‑twin style monitoring (DeepSim site, YC profile).

Who are their target customer(s)

• Chip thermal engineers at semiconductor companies (AI accelerators, CPUs): They need accurate hotspot and full‑chip thermal maps, but current FEM workflows are slow and require manual setup, making iterations and sign‑off lengthy.
• Package and reliability engineers (chip/package/board thermal integrity): They must model heat flow across materials and interfaces at multiple scales, but existing tools force resolution vs. turnaround tradeoffs that delay packaging choices and reliability analysis.
• Design verification teams at SoC/hardware startups: They try many microarchitectural variants and lack time/compute to run high‑fidelity thermal sims for every candidate, leading to skipped checks or late, expensive fixes.
• Data‑center or rack thermal/operations engineers: They need airflow and hotspot modeling across racks/rooms to avoid throttling and over‑provisioning, but current simulations are too slow or low‑resolution for routine operational use.
• Battery‑pack thermal engineers (EVs, energy storage): They need fast, detailed analyses to prevent cell overheating and thermal runaway, yet multi‑physics pack simulations are slow and costly to run repeatedly.

How would they acquire their first 10, 50, and 100 customers

• First 10: Convert current pilots (including the Intel engagement) into paid references via time‑boxed projects with clear success criteria, dedicated engineering support, and deliverables that enable internal sign‑off; publish 1–2 case studies under NDA or publicly (YC profile, DeepSim site).
• First 50: Run targeted technical workshops and a repeatable “3‑week pilot” program for thermal, packaging, and DV teams at tier‑1 semis and fast SoC startups; co‑sell with EDA consultancies/packaging houses and publish reproducible benchmark whitepapers to shorten evaluations (DeepSim site).
• First 100: Productize the pilot into a self‑serve or lightly managed cloud offering with integration adapters; list on major cloud marketplaces and build channel partnerships with EDA vendors, foundries, and SIs while launching vertical plays for batteries and data‑center thermal (DeepSim site, YC profile).

What is the rough total addressable market

Top-down context:

Core near‑term TAM is the semiconductor modeling & simulation market at roughly $4–6B in 2024, which aligns with DeepSim’s current thermal focus (VerifiedMarketReports, SNS Insider). If they expand into adjacent areas like CFD, battery simulation, and data‑center thermal management, the longer‑term platform TAM lands in the ~$15–25B+ range (360iResearch, MarketsandMarkets battery, MarketsandMarkets data‑center cooling).

Bottom-up calculation:

Start with semiconductor simulation buyers (chip/package thermal teams) as the monetizable base consistent with the $4–6B market size; then add adjacent category components—CFD software (~$2–3B), battery simulation (~$1–2B), and data‑center thermal management (~$7–15B)—to reach an aggregate ~$15–25B+ opportunity, acknowledging category overlaps (VerifiedMarketReports, SNS Insider, 360iResearch, MarketsandMarkets, MarketsandMarkets cooling).

Assumptions:

• Market figures reflect software (and in some cases software+services) and are taken at face value from third‑party reports with differing definitions.
• Category additions minimize double‑counting but some overlap (e.g., CFD used in semiconductors) may remain, so ranges are used.
• DeepSim’s product would need additional physics, validation, and integrations to credibly address non‑semiconductor segments.

Who are some of their notable competitors

• ANSYS (Icepak / Ansys Electronics): Incumbent electronics CFD/thermal suite for chip, package, PCB, and system‑level cooling; mature workflows and broad EDA/MCAD integrations make it the default choice for many teams (Icepak).
• Siemens (Simcenter Flotherm): Specialist electronics‑cooling tool for IC packages, PCBs, and enclosures with validated methods and EDA bridges; a direct competitor in package/board thermal workflows (Flotherm).
• COMSOL Multiphysics (Heat Transfer Module): General multiphysics platform that couples heat transfer with electrical/structural physics and enables custom apps; chosen when multiphysics fidelity and customization outweigh turnaround speed (COMSOL).
• Altair (AcuSolve / Altair CFD): General‑purpose CFD and thermal solvers with strong HPC/GPU options for industrial‑scale simulations; competes on solver robustness and enterprise workflows (Altair CFD).
• SimScale: Cloud‑based CAE (CFD + thermal) offering fast, browser‑based runs for electronics cooling and room‑level problems; emphasizes accessibility and cloud scale (SimScale).